2016
Valera, Isabel; Ruiz, Francisco J R; Perez-Cruz, Fernando
Infinite Factorial Unbounded-State Hidden Markov Model Artículo de revista
En: IEEE transactions on pattern analysis and machine intelligence, vol. 38, no 9, pp. 1816 – 1828, 2016, ISSN: 1939-3539.
Resumen | Enlaces | BibTeX | Etiquetas: Bayes methods, Bayesian nonparametrics, CASI CAM CM, Computational modeling, GAMMA-L+ UC3M, Gibbs sampling, Hidden Markov models, Inference algorithms, Journal, Markov processes, Probability distribution, reversible jump Markov chain Monte Carlo, slice sampling, Time series, variational inference, Yttrium
@article{Valera2016b,
title = {Infinite Factorial Unbounded-State Hidden Markov Model},
author = {Isabel Valera and Francisco J R Ruiz and Fernando Perez-Cruz},
url = {http://www.ncbi.nlm.nih.gov/pubmed/26571511 http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true\&arnumber=7322279},
doi = {10.1109/TPAMI.2015.2498931},
issn = {1939-3539},
year = {2016},
date = {2016-09-01},
journal = {IEEE transactions on pattern analysis and machine intelligence},
volume = {38},
number = {9},
pages = {1816 -- 1828},
abstract = {There are many scenarios in artificial intelligence, signal processing or medicine, in which a temporal sequence consists of several unknown overlapping independent causes, and we are interested in accurately recovering those canonical causes. Factorial hidden Markov models (FHMMs) present the versatility to provide a good fit to these scenarios. However, in some scenarios, the number of causes or the number of states of the FHMM cannot be known or limited a priori. In this paper, we propose an infinite factorial unbounded-state hidden Markov model (IFUHMM), in which the number of parallel hidden Markov models (HMMs) and states in each HMM are potentially unbounded. We rely on a Bayesian nonparametric (BNP) prior over integer-valued matrices, in which the columns represent the Markov chains, the rows the time indexes, and the integers the state for each chain and time instant. First, we extend the existent infinite factorial binary-state HMM to allow for any number of states. Then, we modify this model to allow for an unbounded number of states and derive an MCMC-based inference algorithm that properly deals with the trade-off between the unbounded number of states and chains. We illustrate the performance of our proposed models in the power disaggregation problem.},
keywords = {Bayes methods, Bayesian nonparametrics, CASI CAM CM, Computational modeling, GAMMA-L+ UC3M, Gibbs sampling, Hidden Markov models, Inference algorithms, Journal, Markov processes, Probability distribution, reversible jump Markov chain Monte Carlo, slice sampling, Time series, variational inference, Yttrium},
pubstate = {published},
tppubtype = {article}
}
Valera, Isabel; Ruiz, Francisco J R; Perez-Cruz, Fernando
Infinite Factorial Unbounded-State Hidden Markov Model Artículo de revista
En: IEEE transactions on pattern analysis and machine intelligence, vol. To appear, no 99, pp. 1, 2016, ISSN: 1939-3539.
Resumen | Enlaces | BibTeX | Etiquetas: Bayes methods, Bayesian nonparametrics, CASI CAM CM, Computational modeling, GAMMA-L+ UC3M, Gibbs sampling, Hidden Markov models, Inference algorithms, Markov processes, Probability distribution, reversible jump Markov chain Monte Carlo, slice sampling, Time series, variational inference, Yttrium
@article{Valera2016c,
title = {Infinite Factorial Unbounded-State Hidden Markov Model},
author = {Isabel Valera and Francisco J R Ruiz and Fernando Perez-Cruz},
url = {http://www.ncbi.nlm.nih.gov/pubmed/26571511 http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true\&arnumber=7322279},
doi = {10.1109/TPAMI.2015.2498931},
issn = {1939-3539},
year = {2016},
date = {2016-01-01},
journal = {IEEE transactions on pattern analysis and machine intelligence},
volume = {To appear},
number = {99},
pages = {1},
abstract = {There are many scenarios in artificial intelligence, signal processing or medicine, in which a temporal sequence consists of several unknown overlapping independent causes, and we are interested in accurately recovering those canonical causes. Factorial hidden Markov models (FHMMs) present the versatility to provide a good fit to these scenarios. However, in some scenarios, the number of causes or the number of states of the FHMM cannot be known or limited a priori. In this paper, we propose an infinite factorial unbounded-state hidden Markov model (IFUHMM), in which the number of parallel hidden Markov models (HMMs) and states in each HMM are potentially unbounded. We rely on a Bayesian nonparametric (BNP) prior over integer-valued matrices, in which the columns represent the Markov chains, the rows the time indexes, and the integers the state for each chain and time instant. First, we extend the existent infinite factorial binary-state HMM to allow for any number of states. Then, we modify this model to allow for an unbounded number of states and derive an MCMC-based inference algorithm that properly deals with the trade-off between the unbounded number of states and chains. We illustrate the performance of our proposed models in the power disaggregation problem.},
keywords = {Bayes methods, Bayesian nonparametrics, CASI CAM CM, Computational modeling, GAMMA-L+ UC3M, Gibbs sampling, Hidden Markov models, Inference algorithms, Markov processes, Probability distribution, reversible jump Markov chain Monte Carlo, slice sampling, Time series, variational inference, Yttrium},
pubstate = {published},
tppubtype = {article}
}
Borchani, Hanen; Larrañaga, Pedro; Gama, J; Bielza, Concha
Mining multi-dimensional concept-drifting data streams using Bayesian network classifiers Artículo de revista
En: Intelligent Data Analysis, vol. 20, 2016.
Enlaces | BibTeX | Etiquetas: CASI CAM CM, CIG UPM, Journal
@article{Borchani2016,
title = {Mining multi-dimensional concept-drifting data streams using Bayesian network classifiers},
author = {Hanen Borchani and Pedro Larra\~{n}aga and J Gama and Concha Bielza},
url = {http://cig.fi.upm.es/node/879},
year = {2016},
date = {2016-01-01},
journal = {Intelligent Data Analysis},
volume = {20},
keywords = {CASI CAM CM, CIG UPM, Journal},
pubstate = {published},
tppubtype = {article}
}
2015
Valera, Isabel; Ruiz, Francisco J R; Svensson, Lennart; Perez-Cruz, Fernando
Infinite Factorial Dynamical Model Proceedings Article
En: Advances in Neural Information Processing Systems, pp. 1657–1665, Montreal, 2015.
Resumen | Enlaces | BibTeX | Etiquetas: CASI CAM CM, GAMMA-L+ UC3M
@inproceedings{Valera2015a,
title = {Infinite Factorial Dynamical Model},
author = {Isabel Valera and Francisco J R Ruiz and Lennart Svensson and Fernando Perez-Cruz},
url = {http://papers.nips.cc/paper/5667-infinite-factorial-dynamical-model},
year = {2015},
date = {2015-12-01},
booktitle = {Advances in Neural Information Processing Systems},
pages = {1657--1665},
address = {Montreal},
abstract = {We propose the infinite factorial dynamic model (iFDM), a general Bayesian nonparametric model for source separation. Our model builds on the Markov Indian buffet process to consider a potentially unbounded number of hidden Markov chains (sources) that evolve independently according to some dynamics, in which the state space can be either discrete or continuous. For posterior inference, we develop an algorithm based on particle Gibbs with ancestor sampling that can be efficiently applied to a wide range of source separation problems. We evaluate the performance of our iFDM on four well-known applications: multitarget tracking, cocktail party, power disaggregation, and multiuser detection. Our experimental results show that our approach for source separation does not only outperform previous approaches, but it can also handle problems that were computationally intractable for existing approaches.},
keywords = {CASI CAM CM, GAMMA-L+ UC3M},
pubstate = {published},
tppubtype = {inproceedings}
}
Mihaljević, Bojan; Benavides-Piccione, Ruth; Guerra, Luis; DeFelipe, Javier; Larrañaga, Pedro; Bielza, Concha
Classifying GABAergic interneurons with semi-supervised projected model-based clustering. Artículo de revista
En: Artificial intelligence in medicine, vol. 65, no 1, pp. 49–59, 2015, ISSN: 1873-2860.
Resumen | Enlaces | BibTeX | Etiquetas: Automatic neuron classification, CASI CAM CM, Cerebral cortex, CIG UPM, Gaussian mixture models, Journal, Semi-supervised projected clustering
@article{Mihaljevic2015,
title = {Classifying GABAergic interneurons with semi-supervised projected model-based clustering.},
author = {Bojan Mihaljevi\'{c} and Ruth Benavides-Piccione and Luis Guerra and Javier DeFelipe and Pedro Larra\~{n}aga and Concha Bielza},
url = {http://www.aiimjournal.com/article/S0933365714001481/fulltext http://cig.fi.upm.es/articles/2015/Mihaljevic-2015-AIIM.pdf},
doi = {10.1016/j.artmed.2014.12.010},
issn = {1873-2860},
year = {2015},
date = {2015-09-01},
journal = {Artificial intelligence in medicine},
volume = {65},
number = {1},
pages = {49--59},
publisher = {Elsevier},
abstract = {OBJECTIVES: A recently introduced pragmatic scheme promises to be a useful catalog of interneuron names. We sought to automatically classify digitally reconstructed interneuronal morphologies according to this scheme. Simultaneously, we sought to discover possible subtypes of these types that might emerge during automatic classification (clustering). We also investigated which morphometric properties were most relevant for this classification. MATERIALS AND METHODS: A set of 118 digitally reconstructed interneuronal morphologies classified into the common basket (CB), horse-tail (HT), large basket (LB), and Martinotti (MA) interneuron types by 42 of the world's leading neuroscientists, quantified by five simple morphometric properties of the axon and four of the dendrites. We labeled each neuron with the type most commonly assigned to it by the experts. We then removed this class information for each type separately, and applied semi-supervised clustering to those cells (keeping the others' cluster membership fixed), to assess separation from other types and look for the formation of new groups (subtypes). We performed this same experiment unlabeling the cells of two types at a time, and of half the cells of a single type at a time. The clustering model is a finite mixture of Gaussians which we adapted for the estimation of local (per-cluster) feature relevance. We performed the described experiments on three different subsets of the data, formed according to how many experts agreed on type membership: at least 18 experts (the full data set), at least 21 (73 neurons), and at least 26 (47 neurons). RESULTS: Interneurons with more reliable type labels were classified more accurately. We classified HT cells with 100% accuracy, MA cells with 73% accuracy, and CB and LB cells with 56% and 58% accuracy, respectively. We identified three subtypes of the MA type, one subtype of CB and LB types each, and no subtypes of HT (it was a single, homogeneous type). We got maximum (adapted) Silhouette width and ARI values of 1, 0.83, 0.79, and 0.42, when unlabeling the HT, CB, LB, and MA types, respectively, confirming the quality of the formed cluster solutions. The subtypes identified when unlabeling a single type also emerged when unlabeling two types at a time, confirming their validity. Axonal morphometric properties were more relevant that dendritic ones, with the axonal polar histogram length in the [$pi$, 2$pi$) angle interval being particularly useful. CONCLUSIONS: The applied semi-supervised clustering method can accurately discriminate among CB, HT, LB, and MA interneuron types while discovering potential subtypes, and is therefore useful for neuronal classification. The discovery of potential subtypes suggests that some of these types are more heterogeneous that previously thought. Finally, axonal variables seem to be more relevant than dendritic ones for distinguishing among the CB, HT, LB, and MA interneuron types.},
keywords = {Automatic neuron classification, CASI CAM CM, Cerebral cortex, CIG UPM, Gaussian mixture models, Journal, Semi-supervised projected clustering},
pubstate = {published},
tppubtype = {article}
}
Borchani, Hanen; Varando, Gherardo; Bielza, Concha; Larrañaga, Pedro
A survey on multi-output regression Artículo de revista
En: Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 5, no 5, pp. 216–233, 2015, ISSN: 19424787.
Resumen | Enlaces | BibTeX | Etiquetas: algorithm adaptation methods, CASI CAM CM, CIG UPM, Journal, Multi-output regression, multi-target regression, performance evaluation measure, problem transformation methods
@article{Borchani2015,
title = {A survey on multi-output regression},
author = {Hanen Borchani and Gherardo Varando and Concha Bielza and Pedro Larra\~{n}aga},
url = {http://doi.wiley.com/10.1002/widm.1157 http://cig.fi.upm.es/articles/2015/Borchani-2015-WDMKD.pdf},
doi = {10.1002/widm.1157},
issn = {19424787},
year = {2015},
date = {2015-09-01},
journal = {Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery},
volume = {5},
number = {5},
pages = {216--233},
abstract = {In recent years, a plethora of approaches have been proposed to deal with the increasingly challenging task of multi-output regression. This study provides a survey on state-of-the-art multi-output regression methods, that are categorized as problem transformation and algorithm adaptation methods. In addition, we present the mostly used performance evaluation measures, publicly available data sets for multi-output regression real-world problems, as well as open-source software frameworks.},
keywords = {algorithm adaptation methods, CASI CAM CM, CIG UPM, Journal, Multi-output regression, multi-target regression, performance evaluation measure, problem transformation methods},
pubstate = {published},
tppubtype = {article}
}
Varando, Gherardo; Bielza, Concha; Larrañaga, Pedro
Decision functions for chain classifiers based on Bayesian networks for multi-label classification Artículo de revista
En: International Journal of Approximate Reasoning, 2015, ISSN: 0888613X.
Resumen | Enlaces | BibTeX | Etiquetas: CASI CAM CM, CIG UPM, Journal
@article{Varando2015a,
title = {Decision functions for chain classifiers based on Bayesian networks for multi-label classification},
author = {Gherardo Varando and Concha Bielza and Pedro Larra\~{n}aga},
url = {http://www.researchgate.net/publication/279069321_Decision_functions_for_chain_classifiers_based_on_Bayesian_networks_for_multi-label_classification http://cig.fi.upm.es/node/887},
doi = {10.1016/j.ijar.2015.06.006},
issn = {0888613X},
year = {2015},
date = {2015-06-01},
journal = {International Journal of Approximate Reasoning},
abstract = {Multi-label classification problems require each instance to be assigned a subset of a defined set of labels. This problem is equivalent to finding a multi-valued decision function that predicts a vector of binary classes. In this paper we study the decision boundaries of two widely used approaches for building multi-label classifiers, when Bayesian network-augmented naive Bayes classifiers are used as base models: Binary relevance method and chain classifiers. In particular extending previous single-label results to multi-label chain classifiers, we find polynomial expressions for the multi-valued decision functions associated with these methods. We prove upper boundings on the expressive power of both methods and we prove that chain classifiers provide a more expressive model than the binary relevance method Decision functions for chain classifiers based on Bayesian networks for multi-label classification. Available from: http://www.researchgate.net/publication/279069321_Decision_functions_for_chain_classifiers_based_on_Bayesian_networks_for_multi-label_classification [accessed Nov 15, 2015].},
keywords = {CASI CAM CM, CIG UPM, Journal},
pubstate = {published},
tppubtype = {article}
}
Ruiz, Francisco J R; Perez-Cruz, Fernando
A Generative Model for Predicting Outcomes in College Basketball Artículo de revista
En: Journal of Quantitative Analysis in Sports, vol. 11, no 1 Special Issue, pp. 39–52, 2015, ISSN: 1559-0410.
Resumen | Enlaces | BibTeX | Etiquetas: CASI CAM CM, GAMMA-L+ UC3M, Journal, NCAA tournament, Poisson factorization, Probabilistic modeling, variational inference
@article{Ruiz2015b,
title = {A Generative Model for Predicting Outcomes in College Basketball},
author = {Francisco J R Ruiz and Fernando Perez-Cruz},
url = {http://www.degruyter.com/view/j/jqas.2015.11.issue-1/jqas-2014-0055/jqas-2014-0055.xml},
doi = {10.1515/jqas-2014-0055},
issn = {1559-0410},
year = {2015},
date = {2015-03-01},
journal = {Journal of Quantitative Analysis in Sports},
volume = {11},
number = {1 Special Issue},
pages = {39--52},
abstract = {We show that a classical model for soccer can also provide competitive results in predicting basketball outcomes. We modify the classical model in two ways in order to capture both the specific behavior of each National collegiate athletic association (NCAA) conference and different strategies of teams and conferences. Through simulated bets on six online betting houses, we show that this extension leads to better predictive performance in terms of profit we make. We compare our estimates with the probabilities predicted by the winner of the recent Kaggle competition on the 2014 NCAA tournament, and conclude that our model tends to provide results that differ more from the implicit probabilities of the betting houses and, therefore, has the potential to provide higher benefits.},
keywords = {CASI CAM CM, GAMMA-L+ UC3M, Journal, NCAA tournament, Poisson factorization, Probabilistic modeling, variational inference},
pubstate = {published},
tppubtype = {article}
}
Varando, Gherardo; López-Cruz, Pedro L; Nielsen, Thomas D; Larrañaga, Pedro; Bielza, Concha
Conditional Density Approximations with Mixtures of Polynomials Artículo de revista
En: International Journal of Intelligent Systems, vol. 30, no 3, pp. 236–264, 2015, ISSN: 08848173.
Resumen | Enlaces | BibTeX | Etiquetas: CASI CAM CM, CIG UPM, Journal
@article{Varando2015b,
title = {Conditional Density Approximations with Mixtures of Polynomials},
author = {Gherardo Varando and Pedro L L\'{o}pez-Cruz and Thomas D Nielsen and Pedro Larra\~{n}aga and Concha Bielza},
url = {http://doi.wiley.com/10.1002/int.21699 http://cig.fi.upm.es/articles/2015/Varando-2015-IJIS.pdf},
doi = {10.1002/int.21699},
issn = {08848173},
year = {2015},
date = {2015-03-01},
journal = {International Journal of Intelligent Systems},
volume = {30},
number = {3},
pages = {236--264},
abstract = {Mixtures of polynomials (MoPs) are a nonparametric density estimation technique especially designed for hybrid Bayesian networks with continuous and discrete variables. Algorithms to learn one- and multidimensional (marginal) MoPs from data have recently been proposed. In this paper, we introduce two methods for learning MoP approximations of conditional densities from data. Both approaches are based on learning MoP approximations of the joint density and the marginal density of the conditioning variables, but they differ as to how the MoP approximation of the quotient of the two densities is found. We illustrate and study the methods using data sampled from known parametric distributions, and demonstrate their applicability by learning models based on real neuroscience data. Finally, we compare the performance of the proposed methods with an approach for learning mixtures of truncated basis functions (MoTBFs). The empirical results show that the proposed methods generally yield models that are comparable to or significantly better than those found using the MoTBF-based method.},
keywords = {CASI CAM CM, CIG UPM, Journal},
pubstate = {published},
tppubtype = {article}
}
Ruiz, Francisco J R; Perez-Cruz, Fernando
A Generative Model for Predicting Outcomes in College Basketball Artículo de revista
En: Journal of Quantitative Analysis in Sports, vol. 11, no 1 Special Issue, pp. 39–52, 2015, ISSN: 1559-0410.
Resumen | Enlaces | BibTeX | Etiquetas: CASI CAM CM, GAMMA-L+ UC3M, NCAA tournament, Poisson factorization, Probabilistic modeling, variational inference
@article{Ruiz2015bb,
title = {A Generative Model for Predicting Outcomes in College Basketball},
author = {Francisco J R Ruiz and Fernando Perez-Cruz},
url = {http://www.degruyter.com/view/j/jqas.2015.11.issue-1/jqas-2014-0055/jqas-2014-0055.xml},
doi = {10.1515/jqas-2014-0055},
issn = {1559-0410},
year = {2015},
date = {2015-03-01},
journal = {Journal of Quantitative Analysis in Sports},
volume = {11},
number = {1 Special Issue},
pages = {39--52},
abstract = {We show that a classical model for soccer can also provide competitive results in predicting basketball outcomes. We modify the classical model in two ways in order to capture both the specific behavior of each National collegiate athletic association (NCAA) conference and different strategies of teams and conferences. Through simulated bets on six online betting houses, we show that this extension leads to better predictive performance in terms of profit we make. We compare our estimates with the probabilities predicted by the winner of the recent Kaggle competition on the 2014 NCAA tournament, and conclude that our model tends to provide results that differ more from the implicit probabilities of the betting houses and, therefore, has the potential to provide higher benefits.},
keywords = {CASI CAM CM, GAMMA-L+ UC3M, NCAA tournament, Poisson factorization, Probabilistic modeling, variational inference},
pubstate = {published},
tppubtype = {article}
}
Varando, Gherardo; Bielza, Concha; Larrañaga, Pedro
Decision boundary for discrete Bayesian network classifiers Artículo de revista
En: Journal of Machine Learning Research, 2015.
Resumen | Enlaces | BibTeX | Etiquetas: Bayesian networks, CASI CAM CM, CIG UPM, decision boundary, Journal, Lagrange basis, polynomial, supervised classication, threshold function
@article{Varando2015c,
title = {Decision boundary for discrete Bayesian network classifiers},
author = {Gherardo Varando and Concha Bielza and Pedro Larra\~{n}aga},
url = {http://cig.fi.upm.es/node/881 http://cig.fi.upm.es/articles/2015/Varando-2015-JMLR.pdf},
year = {2015},
date = {2015-01-01},
journal = {Journal of Machine Learning Research},
abstract = {Bayesian network classi ers are a powerful machine learning tool. In order to evaluate the expressive power of these models, we compute families of polynomials that sign-represent decision functions induced by Bayesian network classi ers. We prove that those families are linear combinations of products of Lagrange basis polynomials. In absence of V -structures in the predictor sub-graph, we are also able to prove that this family of polynomials does indeed characterize the speci c classi er considered. We then use this representation to bound the number of decision functions representable by Bayesian network classi ers with a given structure},
keywords = {Bayesian networks, CASI CAM CM, CIG UPM, decision boundary, Journal, Lagrange basis, polynomial, supervised classication, threshold function},
pubstate = {published},
tppubtype = {article}
}