Publications
2020
Crecchi, Francesco; de Bodt, Cyril; Bacciu, Davide; Verleysen, Michel; John, Lee
Perplexity-free Parametric t-SNE Conference
Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'20), 2020.
BibTeX | Tags: data visualization, manifold learning, neural networks, unsupervised learning
@conference{esann20Crecchi,
title = { Perplexity-free Parametric t-SNE},
author = {Francesco Crecchi and Cyril de Bodt and Davide Bacciu and Michel Verleysen and Lee John},
editor = {Michel Verleysen},
year = {2020},
date = {2020-04-21},
booktitle = {Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'20)},
keywords = {data visualization, manifold learning, neural networks, unsupervised learning},
pubstate = {published},
tppubtype = {conference}
}
2019
Bacciu, Davide
Reti neurali e linguaggio. Le insidie nascoste di un'algebra delle parole Online
Tavosanis, Mirko (Ed.): Lingua Italiana - Treccani 2019.
Links | BibTeX | Tags: artificial intelligence, natural language processing, neural networks
@online{treccani19,
title = {Reti neurali e linguaggio. Le insidie nascoste di un'algebra delle parole},
author = {Davide Bacciu},
editor = {Mirko Tavosanis},
url = {http://www.treccani.it/magazine/lingua_italiana/speciali/IA/02_Bacciu.html},
year = {2019},
date = {2019-12-03},
organization = {Lingua Italiana - Treccani},
keywords = {artificial intelligence, natural language processing, neural networks},
pubstate = {published},
tppubtype = {online}
}
2018
Davide, Bacciu; Antonio, Bruno
Text Summarization as Tree Transduction by Top-Down TreeLSTM Conference
Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence (SSCI'18), IEEE, 2018.
Abstract | Links | BibTeX | Tags: deep learning, deep learning for graphs, neural networks, structured data processing, tree structured data, tree transductions
@conference{ssci2018,
title = {Text Summarization as Tree Transduction by Top-Down TreeLSTM},
author = {Bacciu Davide and Bruno Antonio},
url = {https://arxiv.org/abs/1809.09096},
doi = {10.1109/SSCI.2018.8628873},
year = {2018},
date = {2018-11-18},
booktitle = {Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence (SSCI'18)},
pages = {1411-1418},
publisher = {IEEE},
abstract = {Extractive compression is a challenging natural language processing problem. This work contributes by formulating neural extractive compression as a parse tree transduction problem, rather than a sequence transduction task. Motivated by this, we introduce a deep neural model for learning structure-to-substructure tree transductions by extending the standard Long Short-Term Memory, considering the parent-child relationships in the structural recursion. The proposed model can achieve state of the art performance on sentence compression benchmarks, both in terms of accuracy and compression rate. },
keywords = {deep learning, deep learning for graphs, neural networks, structured data processing, tree structured data, tree transductions},
pubstate = {published},
tppubtype = {conference}
}
Extractive compression is a challenging natural language processing problem. This work contributes by formulating neural extractive compression as a parse tree transduction problem, rather than a sequence transduction task. Motivated by this, we introduce a deep neural model for learning structure-to-substructure tree transductions by extending the standard Long Short-Term Memory, considering the parent-child relationships in the structural recursion. The proposed model can achieve state of the art performance on sentence compression benchmarks, both in terms of accuracy and compression rate. Marco, Podda; Davide, Bacciu; Alessio, Micheli; Roberto, Bellu; Giulia, Placidi; Luigi, Gagliardi
A machine learning approach to estimating preterm infants survival: development of the Preterm Infants Survival Assessment (PISA) predictor Journal Article
In: Nature Scientific Reports, vol. 8, 2018.
Abstract | Links | BibTeX | Tags: bioinformatics, biomedical data, neural networks, support vector machine
@article{naturescirep2018,
title = {A machine learning approach to estimating preterm infants survival: development of the Preterm Infants Survival Assessment (PISA) predictor},
author = {Podda Marco and Bacciu Davide and Micheli Alessio and Bellu Roberto and Placidi Giulia and Gagliardi Luigi },
url = {https://doi.org/10.1038/s41598-018-31920-6},
doi = {10.1038/s41598-018-31920-6},
year = {2018},
date = {2018-09-13},
journal = {Nature Scientific Reports},
volume = {8},
abstract = {Estimation of mortality risk of very preterm neonates is carried out in clinical and research settings. We aimed at elaborating a prediction tool using machine learning methods. We developed models on a cohort of 23747 neonates <30 weeks gestational age, or <1501 g birth weight, enrolled in the Italian Neonatal Network in 2008–2014 (development set), using 12 easily collected perinatal variables. We used a cohort from 2015–2016 (N = 5810) as a test set. Among several machine learning methods we chose artificial Neural Networks (NN). The resulting predictor was compared with logistic regression models. In the test cohort, NN had a slightly better discrimination than logistic regression (P < 0.002). The differences were greater in subgroups of neonates (at various gestational age or birth weight intervals, singletons). Using a cutoff of death probability of 0.5, logistic regression misclassified 67/5810 neonates (1.2 percent) more than NN. In conclusion our study – the largest published so far – shows that even in this very simplified scenario, using only limited information available up to 5 minutes after birth, a NN approach had a small but significant advantage over current approaches. The software implementing the predictor is made freely available to the community.},
keywords = {bioinformatics, biomedical data, neural networks, support vector machine},
pubstate = {published},
tppubtype = {article}
}
Estimation of mortality risk of very preterm neonates is carried out in clinical and research settings. We aimed at elaborating a prediction tool using machine learning methods. We developed models on a cohort of 23747 neonates <30 weeks gestational age, or <1501 g birth weight, enrolled in the Italian Neonatal Network in 2008–2014 (development set), using 12 easily collected perinatal variables. We used a cohort from 2015–2016 (N = 5810) as a test set. Among several machine learning methods we chose artificial Neural Networks (NN). The resulting predictor was compared with logistic regression models. In the test cohort, NN had a slightly better discrimination than logistic regression (P < 0.002). The differences were greater in subgroups of neonates (at various gestational age or birth weight intervals, singletons). Using a cutoff of death probability of 0.5, logistic regression misclassified 67/5810 neonates (1.2 percent) more than NN. In conclusion our study – the largest published so far – shows that even in this very simplified scenario, using only limited information available up to 5 minutes after birth, a NN approach had a small but significant advantage over current approaches. The software implementing the predictor is made freely available to the community.2011
H, Jarman Ian; A, Etchells Terence; Davide, Bacciu; M, Garibaldi John; O, Ellis Ian; JG, Lisboa Paulo
Clustering of protein expression data: a benchmark of statistical and neural approaches Journal Article
In: Soft Computing-A Fusion of Foundations, Methodologies and Applications, vol. 15, no. 8, pp. 1459–1469, 2011, ISSN: 1432-7643.
Links | BibTeX | Tags: biomedical data, clustering, neural networks, statistics, unsupervised learning
@article{soco2011,
title = {Clustering of protein expression data: a benchmark of statistical and neural approaches},
author = {Jarman Ian H and Etchells Terence A and Bacciu Davide and Garibaldi John M and Ellis Ian O and Lisboa Paulo JG},
url = {http://dx.doi.org/10.1007/s00500-010-0596-9},
doi = {10.1007/s00500-010-0596-9},
issn = {1432-7643},
year = {2011},
date = {2011-01-01},
journal = {Soft Computing-A Fusion of Foundations, Methodologies and Applications},
volume = {15},
number = {8},
pages = {1459--1469},
publisher = {Springer},
keywords = {biomedical data, clustering, neural networks, statistics, unsupervised learning},
pubstate = {published},
tppubtype = {article}
}
2010
S, Fernandes Ana; Davide, Bacciu; H, Jarman Ian; A, Etchells Terence; M, Fonseca Jose; JG, Lisboa Paulo
Different Methodologies for Patient Stratification Using Survival Data Conference
Lecture Notes in Computer ScienceComputational Intelligence Methods for Bioinformatics and Biostatistics, vol. 6160, 2010.
Abstract | Links | BibTeX | Tags: biomedical data, clustering, competitive repetition suppression learning, neural networks, statistics, survival analysis, unsupervised learning
@conference{11568_465483,
title = {Different Methodologies for Patient Stratification Using Survival Data},
author = {Fernandes Ana S and Bacciu Davide and Jarman Ian H and Etchells Terence A and Fonseca Jose M and Lisboa Paulo JG},
doi = {10.1007/978-3-642-14571-1_21},
year = {2010},
date = {2010-01-01},
booktitle = {Lecture Notes in Computer ScienceComputational Intelligence Methods for Bioinformatics and Biostatistics},
journal = {LECTURE NOTES IN COMPUTER SCIENCE},
volume = {6160},
pages = {276--290},
abstract = {Clinical characterization of breast cancer patients related to their risk and profiles is an important part for making their correct prognostic assessments. This paper first proposes a prognostic index obtained when it is applied a flexible non-linear time-to-event model and compares it to a widely used linear survival estimator. This index underpins different stratification methodologies including informed clustering utilising the principle of learning metrics, regression trees and recursive application of the log-rank test. Missing data issue was overcome using multiple imputation, which was applied to a neural network model of survival fitted to a data set for breast cancer (n=743). It was found the three methodologies broadly agree, having however important differences.},
keywords = {biomedical data, clustering, competitive repetition suppression learning, neural networks, statistics, survival analysis, unsupervised learning},
pubstate = {published},
tppubtype = {conference}
}
Clinical characterization of breast cancer patients related to their risk and profiles is an important part for making their correct prognostic assessments. This paper first proposes a prognostic index obtained when it is applied a flexible non-linear time-to-event model and compares it to a widely used linear survival estimator. This index underpins different stratification methodologies including informed clustering utilising the principle of learning metrics, regression trees and recursive application of the log-rank test. Missing data issue was overcome using multiple imputation, which was applied to a neural network model of survival fitted to a data set for breast cancer (n=743). It was found the three methodologies broadly agree, having however important differences.2009
Davide, Bacciu; Antonina, Starita
Expansive competitive learning for kernel vector quantization Journal Article
In: Pattern Recognition Letters, vol. 30, no. 6, pp. 641–651, 2009, ISSN: 0167-8655.
Links | BibTeX | Tags: clustering, competitive repetition suppression learning, kernel methods, neural networks, statistics, unsupervised learning
@article{patrec2009,
title = {Expansive competitive learning for kernel vector quantization},
author = {Bacciu Davide and Starita Antonina},
url = {http://dx.doi.org/10.1016/j.patrec.2009.01.002},
doi = {10.1016/j.patrec.2009.01.002},
issn = {0167-8655},
year = {2009},
date = {2009-01-01},
journal = {Pattern Recognition Letters},
volume = {30},
number = {6},
pages = {641--651},
publisher = {Elsevier},
keywords = {clustering, competitive repetition suppression learning, kernel methods, neural networks, statistics, unsupervised learning},
pubstate = {published},
tppubtype = {article}
}
JG, Lisboa Paulo; H, Jarman Ian; A, Etchells Terence; Davide, Bacciu; M, Garibaldi John
Model-based and model-free clustering: a case study of protein expression data for breast cancer Conference
PROCEEDINGS OF THE 2009 UK WORKSHOP ON COMPUTATIONAL INTELLIGENCE, 2009.
BibTeX | Tags: biomedical data, clustering, competitive repetition suppression learning, feature selection, neural networks, unsupervised learning
@conference{11568_466869,
title = {Model-based and model-free clustering: a case study of protein expression data for breast cancer},
author = {Lisboa Paulo JG and Jarman Ian H and Etchells Terence A and Bacciu Davide and Garibaldi John M},
year = {2009},
date = {2009-01-01},
booktitle = {PROCEEDINGS OF THE 2009 UK WORKSHOP ON COMPUTATIONAL INTELLIGENCE},
keywords = {biomedical data, clustering, competitive repetition suppression learning, feature selection, neural networks, unsupervised learning},
pubstate = {published},
tppubtype = {conference}
}
S, Fernandes Ana; Davide, Bacciu; H, Jarman Ian; A, Etchells Terence; M, Fonseca Jose; Lisboa, Paulo J G
p-Health in Breast Oncology: A Framework for Predictive and Participatory e-Systems Conference
2009 Second International Conference on Developments in eSystems Engineering, IEEE, 2009.
Abstract | Links | BibTeX | Tags: biomedical data, clustering, neural networks
@conference{11568_465485,
title = {p-Health in Breast Oncology: A Framework for Predictive and Participatory e-Systems},
author = { Fernandes Ana S and Bacciu Davide and Jarman Ian H and Etchells Terence A and Fonseca Jose M and Paulo J G Lisboa},
doi = {10.1109/DeSE.2009.68},
year = {2009},
date = {2009-01-01},
booktitle = {2009 Second International Conference on Developments in eSystems Engineering},
pages = {123--129},
publisher = {IEEE},
abstract = {Maintaining the financial sustainability of healthcare provision makes developments in e-Systems of the utmost priority in healthcare. In particular, it leads to a radical review of healthcare delivery for the future as personalised, preventive, predictive and participatory, or p-Health. It is a vision that places e-Systems at the core of healthcare delivery, in contrast to current practice. This view of the demands of the 21st century sets an agenda that builds upon advances in engineering devices and computing infrastructure, but also computational intelligence and new models for communication between healthcare providers and the public. This paper gives an overview of p-Health with reference to decision support in breast cancer.},
keywords = {biomedical data, clustering, neural networks},
pubstate = {published},
tppubtype = {conference}
}
Maintaining the financial sustainability of healthcare provision makes developments in e-Systems of the utmost priority in healthcare. In particular, it leads to a radical review of healthcare delivery for the future as personalised, preventive, predictive and participatory, or p-Health. It is a vision that places e-Systems at the core of healthcare delivery, in contrast to current practice. This view of the demands of the 21st century sets an agenda that builds upon advances in engineering devices and computing infrastructure, but also computational intelligence and new models for communication between healthcare providers and the public. This paper gives an overview of p-Health with reference to decision support in breast cancer. Davide, Bacciu; H, Jarman Ian; A, Etchells Terence; G, Lisboa Paulo J
Patient stratification with competing risks by multivariate Fisher distance Conference
2009 International Joint Conference on Neural Networks, IEEE, 2009.
Abstract | Links | BibTeX | Tags: biomedical data, clustering, competitive repetition suppression learning, generative model, neural networks, statistics, survival analysis, unsupervised learning
@conference{11568_465484,
title = {Patient stratification with competing risks by multivariate Fisher distance},
author = {Bacciu Davide and Jarman Ian H and Etchells Terence A and Lisboa Paulo J G},
doi = {10.1109/IJCNN.2009.5179077},
year = {2009},
date = {2009-01-01},
booktitle = {2009 International Joint Conference on Neural Networks},
pages = {3453--3460},
publisher = {IEEE},
abstract = {Early characterization of patients with respect to their predicted response to treatment is a fundamental step towards the delivery of effective, personalized care. Starting from the results of a time-to-event model with competing risks using the framework of partial logistic artificial neural networks with automatic relevance determination (PLANNCR-ARD), we discuss an effective semi-supervised approach to patient stratification with application to Acute Myeloid Leukaemia (AML) data (n = 509) acquired prospectively by the GIMEMA consortium. Multiple prognostic indices provided by the survival model are exploited to build a metric based on the Fisher information matrix. Cluster number estimation is then performed in the Fisher-induced affine space, yielding to the discovery of a stratification of the patients into groups characterized by significantly different mortality risks following induction therapy in AML. The proposed model is shown to be able to cluster the input data, while promoting specificity of both target outcomes, namely Complete Remission (CR) and Induction Death (ID). This generic clustering methodology generates an affine transformation of the data space that is coherent with the prognostic information predicted by the PLANNCR-ARD model.},
keywords = {biomedical data, clustering, competitive repetition suppression learning, generative model, neural networks, statistics, survival analysis, unsupervised learning},
pubstate = {published},
tppubtype = {conference}
}
Early characterization of patients with respect to their predicted response to treatment is a fundamental step towards the delivery of effective, personalized care. Starting from the results of a time-to-event model with competing risks using the framework of partial logistic artificial neural networks with automatic relevance determination (PLANNCR-ARD), we discuss an effective semi-supervised approach to patient stratification with application to Acute Myeloid Leukaemia (AML) data (n = 509) acquired prospectively by the GIMEMA consortium. Multiple prognostic indices provided by the survival model are exploited to build a metric based on the Fisher information matrix. Cluster number estimation is then performed in the Fisher-induced affine space, yielding to the discovery of a stratification of the patients into groups characterized by significantly different mortality risks following induction therapy in AML. The proposed model is shown to be able to cluster the input data, while promoting specificity of both target outcomes, namely Complete Remission (CR) and Induction Death (ID). This generic clustering methodology generates an affine transformation of the data space that is coherent with the prognostic information predicted by the PLANNCR-ARD model.2008
Davide, Bacciu; Antonina, Starita
Competitive Repetition Suppression (CoRe) Clustering: A Biologically Inspired Learning Model With Application to Robust Clustering Journal Article
In: Neural Networks, IEEE Transactions on, vol. 19, no. 11, pp. 1922 -1941, 2008, ISSN: 1045-9227.
Links | BibTeX | Tags: biologically inspired learning, clustering, competitive repetition suppression learning, neural networks, soft competitive learning, unsupervised learning
@article{coreTNN2008,
title = {Competitive Repetition Suppression (CoRe) Clustering: A Biologically Inspired Learning Model With Application to Robust Clustering},
author = {Bacciu Davide and Starita Antonina},
url = {http://dx.doi.org/10.1016/j.patrec.2009.01.002},
doi = {10.1109/TNN.2008.2004407},
issn = {1045-9227},
year = {2008},
date = {2008-11-01},
journal = {Neural Networks, IEEE Transactions on},
volume = {19},
number = {11},
pages = {1922 -1941},
keywords = {biologically inspired learning, clustering, competitive repetition suppression learning, neural networks, soft competitive learning, unsupervised learning},
pubstate = {published},
tppubtype = {article}
}
Davide, Bacciu
A Perceptual Learning Model to Discover the Hierarchical Latent Structure of Image Collections PhD Thesis
2008.
Abstract | Links | BibTeX | Tags: biomedical data, clustering, competitive repetition suppression learning, feature selection, generative model, graphical models, image understanding, latent topic model, neural networks, statistics, unsupervised learning
@phdthesis{11568_466874,
title = {A Perceptual Learning Model to Discover the Hierarchical Latent Structure of Image Collections},
author = { Bacciu Davide},
url = {http://e-theses.imtlucca.it/id/eprint/7},
doi = {10.6092/imtlucca/e-theses/7},
year = {2008},
date = {2008-01-01},
publisher = {IMT Lucca},
abstract = {Biology has been an unparalleled source of inspiration for the work of researchers in several scientific and engineering fields including computer vision. The starting point of this thesis is the neurophysiological properties of the human early visual system, in particular, the cortical mechanism that mediates learning by exploiting information about stimuli repetition. Repetition has long been considered a fundamental correlate of skill acquisition andmemory formation in biological aswell as computational learning models. However, recent studies have shown that biological neural networks have differentways of exploiting repetition in forming memory maps. The thesis focuses on a perceptual learning mechanism called repetition suppression, which exploits the temporal distribution of neural activations to drive an efficient neural allocation for a set of stimuli. This explores the neurophysiological hypothesis that repetition suppression serves as an unsupervised perceptual learning mechanism that can drive efficient memory formation by reducing the overall size of stimuli representation while strengthening the responses of the most selective neurons. This interpretation of repetition is different from its traditional role in computational learning models mainly to induce convergence and reach training stability, without using this information to provide focus for the neural representations of the data. The first part of the thesis introduces a novel computational model with repetition suppression, which forms an unsupervised competitive systemtermed CoRe, for Competitive Repetition-suppression learning. The model is applied to generalproblems in the fields of computational intelligence and machine learning. Particular emphasis is placed on validating the model as an effective tool for the unsupervised exploration of bio-medical data. In particular, it is shown that the repetition suppression mechanism efficiently addresses the issues of automatically estimating the number of clusters within the data, as well as filtering noise and irrelevant input components in highly dimensional data, e.g. gene expression levels from DNA Microarrays. The CoRe model produces relevance estimates for the each covariate which is useful, for instance, to discover the best discriminating bio-markers. The description of the model includes a theoretical analysis using Huber’s robust statistics to show that the model is robust to outliers and noise in the data. The convergence properties of themodel also studied. It is shown that, besides its biological underpinning, the CoRe model has useful properties in terms of asymptotic behavior. By exploiting a kernel-based formulation for the CoRe learning error, a theoretically sound motivation is provided for the model’s ability to avoid local minima of its loss function. To do this a necessary and sufficient condition for global error minimization in vector quantization is generalized by extending it to distance metrics in generic Hilbert spaces. This leads to the derivation of a family of kernel-based algorithms that address the local minima issue of unsupervised vector quantization in a principled way. The experimental results show that the algorithm can achieve a consistent performance gain compared with state-of-the-art learning vector quantizers, while retaining a lower computational complexity (linear with respect to the dataset size). Bridging the gap between the low level representation of the visual content and the underlying high-level semantics is a major research issue of current interest. The second part of the thesis focuses on this problem by introducing a hierarchical and multi-resolution approach to visual content understanding. On a spatial level, CoRe learning is used to pool together the local visual patches by organizing them into perceptually meaningful intermediate structures. On the semantical level, it provides an extension of the probabilistic Latent Semantic Analysis (pLSA) model that allows discovery and organization of the visual topics into a hierarchy of aspects. The proposed hierarchical pLSA model is shown to effectively address the unsupervised discovery of relevant visual classes from pictorial collections, at the same time learning to segment the image regions containing the discovered classes. Furthermore, by drawing on a recent pLSA-based image annotation system, the hierarchical pLSA model is extended to process and representmulti-modal collections comprising textual and visual data. The results of the experimental evaluation show that the proposed model learns to attach textual labels (available only at the level of the whole image) to the discovered image regions, while increasing the precision/ recall performance with respect to flat, pLSA annotation model.},
keywords = {biomedical data, clustering, competitive repetition suppression learning, feature selection, generative model, graphical models, image understanding, latent topic model, neural networks, statistics, unsupervised learning},
pubstate = {published},
tppubtype = {phdthesis}
}
Biology has been an unparalleled source of inspiration for the work of researchers in several scientific and engineering fields including computer vision. The starting point of this thesis is the neurophysiological properties of the human early visual system, in particular, the cortical mechanism that mediates learning by exploiting information about stimuli repetition. Repetition has long been considered a fundamental correlate of skill acquisition andmemory formation in biological aswell as computational learning models. However, recent studies have shown that biological neural networks have differentways of exploiting repetition in forming memory maps. The thesis focuses on a perceptual learning mechanism called repetition suppression, which exploits the temporal distribution of neural activations to drive an efficient neural allocation for a set of stimuli. This explores the neurophysiological hypothesis that repetition suppression serves as an unsupervised perceptual learning mechanism that can drive efficient memory formation by reducing the overall size of stimuli representation while strengthening the responses of the most selective neurons. This interpretation of repetition is different from its traditional role in computational learning models mainly to induce convergence and reach training stability, without using this information to provide focus for the neural representations of the data. The first part of the thesis introduces a novel computational model with repetition suppression, which forms an unsupervised competitive systemtermed CoRe, for Competitive Repetition-suppression learning. The model is applied to generalproblems in the fields of computational intelligence and machine learning. Particular emphasis is placed on validating the model as an effective tool for the unsupervised exploration of bio-medical data. In particular, it is shown that the repetition suppression mechanism efficiently addresses the issues of automatically estimating the number of clusters within the data, as well as filtering noise and irrelevant input components in highly dimensional data, e.g. gene expression levels from DNA Microarrays. The CoRe model produces relevance estimates for the each covariate which is useful, for instance, to discover the best discriminating bio-markers. The description of the model includes a theoretical analysis using Huber’s robust statistics to show that the model is robust to outliers and noise in the data. The convergence properties of themodel also studied. It is shown that, besides its biological underpinning, the CoRe model has useful properties in terms of asymptotic behavior. By exploiting a kernel-based formulation for the CoRe learning error, a theoretically sound motivation is provided for the model’s ability to avoid local minima of its loss function. To do this a necessary and sufficient condition for global error minimization in vector quantization is generalized by extending it to distance metrics in generic Hilbert spaces. This leads to the derivation of a family of kernel-based algorithms that address the local minima issue of unsupervised vector quantization in a principled way. The experimental results show that the algorithm can achieve a consistent performance gain compared with state-of-the-art learning vector quantizers, while retaining a lower computational complexity (linear with respect to the dataset size). Bridging the gap between the low level representation of the visual content and the underlying high-level semantics is a major research issue of current interest. The second part of the thesis focuses on this problem by introducing a hierarchical and multi-resolution approach to visual content understanding. On a spatial level, CoRe learning is used to pool together the local visual patches by organizing them into perceptually meaningful intermediate structures. On the semantical level, it provides an extension of the probabilistic Latent Semantic Analysis (pLSA) model that allows discovery and organization of the visual topics into a hierarchy of aspects. The proposed hierarchical pLSA model is shown to effectively address the unsupervised discovery of relevant visual classes from pictorial collections, at the same time learning to segment the image regions containing the discovered classes. Furthermore, by drawing on a recent pLSA-based image annotation system, the hierarchical pLSA model is extended to process and representmulti-modal collections comprising textual and visual data. The results of the experimental evaluation show that the proposed model learns to attach textual labels (available only at the level of the whole image) to the discovered image regions, while increasing the precision/ recall performance with respect to flat, pLSA annotation model. Davide, BACCIU; Elia, BIGANZOLI; JG, LISBOA Paulo; Antonina, Starita
Are Model-based Clustering and Neural Clustering Consistent? A Case Study from Bioinformatics Conference
Proceedings of the 12th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES'08), vol. 5178, Springer, 2008.
Abstract | Links | BibTeX | Tags: biomedical data, clustering, competitive repetition suppression learning, feature selection, neural networks, statistics, unsupervised learning
@conference{11568_465487,
title = {Are Model-based Clustering and Neural Clustering Consistent? A Case Study from Bioinformatics},
author = {BACCIU Davide and BIGANZOLI Elia and LISBOA Paulo JG and Starita Antonina},
doi = {10.1007/978-3-540-85565-1-23},
year = {2008},
date = {2008-01-01},
booktitle = {Proceedings of the 12th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES'08)},
journal = {LECTURE NOTES IN COMPUTER SCIENCE},
volume = {5178},
pages = {181--188},
publisher = {Springer},
abstract = {A novel neural network clustering algorithm, CoRe, is benchmarked against previously published results on a breast cancer data set and applying the method of Partition Around Medoids (PAM). The data serve to compare the samples partitions obtained with the neural network, PAM and model-based algorithms, namely Gaussian Mixture Model (GMM), Variational Bayesian Gaussian Mixture (VBG) and Variational Bayesian Mixtures with Splitting (VBS). It is found that CoRe, on the one hand, agrees with the previously published partitions; on the other hand, it supports the existence of a supplementary cluster that we hypothesize to be an additional tumor subgroup with respect to those previously identified by PAM},
keywords = {biomedical data, clustering, competitive repetition suppression learning, feature selection, neural networks, statistics, unsupervised learning},
pubstate = {published},
tppubtype = {conference}
}
A novel neural network clustering algorithm, CoRe, is benchmarked against previously published results on a breast cancer data set and applying the method of Partition Around Medoids (PAM). The data serve to compare the samples partitions obtained with the neural network, PAM and model-based algorithms, namely Gaussian Mixture Model (GMM), Variational Bayesian Gaussian Mixture (VBG) and Variational Bayesian Mixtures with Splitting (VBS). It is found that CoRe, on the one hand, agrees with the previously published partitions; on the other hand, it supports the existence of a supplementary cluster that we hypothesize to be an additional tumor subgroup with respect to those previously identified by PAM Davide, Bacciu; Antonina, Starita
Convergence Behavior of Competitive Repetition-Suppression Clustering Conference
Neural Information Processing, Lecture Notes in Computer Science, vol. 4984, Springer, 2008.
Abstract | Links | BibTeX | Tags: clustering, competitive repetition suppression learning, neural networks, statistics, unsupervised learning
@conference{11568_466667,
title = {Convergence Behavior of Competitive Repetition-Suppression Clustering},
author = {Bacciu Davide and Starita Antonina },
doi = {10.1007/978-3-540-69158-7_52},
year = {2008},
date = {2008-01-01},
booktitle = {Neural Information Processing, Lecture Notes in Computer Science},
volume = {4984},
pages = {497--506},
publisher = {Springer},
abstract = {Competitive Repetition-suppression (CoRe) clustering is a bio-inspired learning algorithm that is capable of automatically determining the unknown cluster number from the data. In a previous work it has been shown how CoRe clustering represents a robust generalization of rival penalized competitive learning (RPCL) by means of M-estimators. This paper studies the convergence behavior of the CoRe model, based on the analysis proposed for the distance-sensitive RPCL (DSRPCL) algorithm. Furthermore, it is proposed a global minimum criterion for learning vector quantization in kernel space that is used to assess the correct location property for the CoRe algorithm.},
keywords = {clustering, competitive repetition suppression learning, neural networks, statistics, unsupervised learning},
pubstate = {published},
tppubtype = {conference}
}
Competitive Repetition-suppression (CoRe) clustering is a bio-inspired learning algorithm that is capable of automatically determining the unknown cluster number from the data. In a previous work it has been shown how CoRe clustering represents a robust generalization of rival penalized competitive learning (RPCL) by means of M-estimators. This paper studies the convergence behavior of the CoRe model, based on the analysis proposed for the distance-sensitive RPCL (DSRPCL) algorithm. Furthermore, it is proposed a global minimum criterion for learning vector quantization in kernel space that is used to assess the correct location property for the CoRe algorithm.2007
Davide, Bacciu; Alessio, Micheli; Antonina, Starita
Feature-wise Competitive Repetition Suppression Learning for Gene Data Clustering and Feature Ranking Technical Report
Università di Pisa 2007.
Links | BibTeX | Tags: biomedical data, clustering, competitive repetition suppression learning, feature selection, kernel methods, neural networks
@techreport{11568_255939,
title = {Feature-wise Competitive Repetition Suppression Learning for Gene Data Clustering and Feature Ranking},
author = {Bacciu Davide and Micheli Alessio and Starita Antonina},
url = {http://compass2.di.unipi.it/TR/Files/TR-07-04.pdf.gz},
year = {2007},
date = {2007-01-01},
volume = {TR-07-04},
pages = {1--14},
institution = {Università di Pisa},
keywords = {biomedical data, clustering, competitive repetition suppression learning, feature selection, kernel methods, neural networks},
pubstate = {published},
tppubtype = {techreport}
}