Probabilistic Models for Clustering

[1]  Geoffrey J. McLachlan,et al.  Mixture models : inference and applications to clustering , 1989 .

[2]  H. Malcolm Hudson,et al.  Accelerated image reconstruction using ordered subsets of projection data , 1994, IEEE Trans. Medical Imaging.

[3]  P. Embrechts,et al.  Quantitative Risk Management: Concepts, Techniques, and Tools , 2005 .

[4]  Hyeyoung Park,et al.  Singularity and Slow Convergence of the EM algorithm for Gaussian Mixtures , 2009, Neural Processing Letters.

[5]  Thomas L. Griffiths,et al.  Integrating Topics and Syntax , 2004, NIPS.

[6]  Paul F. Lazarsfeld,et al.  Latent Structure Analysis. , 1969 .

[7]  Gérard Govaert,et al.  An improvement of the NEC criterion for assessing the number of clusters in a mixture model , 1999, Pattern Recognit. Lett..

[8]  David R. Anderson,et al.  Model selection and multimodel inference : a practical information-theoretic approach , 2003 .

[9]  Adrian E. Raftery,et al.  How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis , 1998, Comput. J..

[10]  Francis R. Bach,et al.  Online Learning for Latent Dirichlet Allocation , 2010, NIPS.

[11]  Adrian E. Raftery,et al.  Inference in model-based cluster analysis , 1997, Stat. Comput..

[12]  L. Baum,et al.  A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains , 1970 .

[13]  Thomas Hofmann,et al.  Probabilistic Latent Semantic Analysis , 1999, UAI.

[14]  G. Schwarz Estimating the Dimension of a Model , 1978 .

[15]  Douglas A. Reynolds,et al.  Robust text-independent speaker identification using Gaussian mixture speaker models , 1995, IEEE Trans. Speech Audio Process..

[16]  Haim H. Permuter,et al.  A study of Gaussian mixture models of color and texture features for image classification and segmentation , 2006, Pattern Recognit..

[17]  Jeff A. Bilmes,et al.  A gentle tutorial of the em algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models , 1998 .

[18]  L. Wasserman,et al.  Practical Bayesian Density Estimation Using Mixtures of Normals , 1997 .

[19]  Sylvia Richardson,et al.  Markov Chain Monte Carlo in Practice , 1997 .

[20]  Christophe Biernacki,et al.  Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models , 2003, Comput. Stat. Data Anal..

[21]  Tom Minka,et al.  Expectation-Propogation for the Generative Aspect Model , 2002, UAI.

[22]  Gérard Govaert,et al.  Assessing a Mixture Model for Clustering with the Integrated Completed Likelihood , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[23]  Sean Borman,et al.  The Expectation Maximization Algorithm A short tutorial , 2006 .

[24]  Carl E. Rasmussen,et al.  The Infinite Gaussian Mixture Model , 1999, NIPS.

[25]  A. F. Smith,et al.  Statistical analysis of finite mixture distributions , 1986 .

[26]  Hanna M. Wallach,et al.  Topic modeling: beyond bag-of-words , 2006, ICML.

[27]  Geoffrey E. Hinton,et al.  Using Generative Models for Handwritten Digit Recognition , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[28]  Thomas L. Griffiths,et al.  Probabilistic Topic Models , 2007 .

[29]  Nando de Freitas,et al.  An Introduction to MCMC for Machine Learning , 2004, Machine Learning.

[30]  Michael I. Jordan,et al.  An Introduction to Variational Methods for Graphical Models , 1999, Machine Learning.

[31]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[32]  Andrew McCallum,et al.  Topic Models Conditioned on Arbitrary Features with Dirichlet-multinomial Regression , 2008, UAI.

[33]  Max Welling,et al.  Fast collapsed gibbs sampling for latent dirichlet allocation , 2008, KDD.

[34]  P. Green,et al.  Corrigendum: On Bayesian analysis of mixtures with an unknown number of components , 1997 .

[35]  Nikolas P. Galatsanos,et al.  A spatially constrained mixture model for image segmentation , 2005, IEEE Transactions on Neural Networks.

[36]  Thomas L. Griffiths,et al.  The nested chinese restaurant process and bayesian nonparametric inference of topic hierarchies , 2007, JACM.

[37]  David M. Blei,et al.  Probabilistic topic models , 2012, Commun. ACM.

[38]  G. Celeux,et al.  An entropy criterion for assessing the number of clusters in a mixture model , 1996 .

[39]  Harold W. Sorenson,et al.  Parameter estimation: Principles and problems , 1980 .

[40]  Thomas Hofmann,et al.  Learning from Dyadic Data , 1998, NIPS.

[41]  G. McLachlan,et al.  The EM algorithm and extensions , 1996 .

[42]  David L. Dowe,et al.  Minimum Message Length and Kolmogorov Complexity , 1999, Comput. J..

[43]  Hinrich Schütze,et al.  Introduction to information retrieval , 2008 .

[44]  Joydeep Ghosh,et al.  Under Consideration for Publication in Knowledge and Information Systems Generative Model-based Document Clustering: a Comparative Study , 2003 .

[45]  Chin-Hui Lee,et al.  Maximum a posteriori estimation for multivariate Gaussian mixture observations of Markov chains , 1994, IEEE Trans. Speech Audio Process..

[46]  S. Ng,et al.  Robust Cluster Analysis via Mixture Models , 2006 .

[47]  P. Green Bayesian reconstructions from emission tomography data using a modified EM algorithm. , 1990, IEEE transactions on medical imaging.

[48]  G. Celeux,et al.  A Classification EM algorithm for clustering and two stochastic versions , 1992 .

[49]  Adrian F. M. Smith,et al.  Sampling-Based Approaches to Calculating Marginal Densities , 1990 .

[50]  Linda Kaufman,et al.  Implementing and Accelerating the EM Algorithm for Positron Emission Tomography , 1987, IEEE Transactions on Medical Imaging.

[51]  R. Tibshirani,et al.  Discriminant Analysis by Gaussian Mixtures , 1996 .

[52]  David M. Blei,et al.  Relational Topic Models for Document Networks , 2009, AISTATS.

[53]  Geoffrey E. Hinton,et al.  A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants , 1998, Learning in Graphical Models.

[54]  John D. Lafferty,et al.  Dynamic topic models , 2006, ICML.

[55]  Anil K. Jain,et al.  Unsupervised Learning of Finite Mixture Models , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[56]  Geoffrey E. Hinton,et al.  SMEM Algorithm for Mixture Models , 1998, Neural Computation.

[57]  Yee Whye Teh,et al.  Sharing Clusters among Related Groups: Hierarchical Dirichlet Processes , 2004, NIPS.

[58]  J. Rissanen A UNIVERSAL PRIOR FOR INTEGERS AND ESTIMATION BY MINIMUM DESCRIPTION LENGTH , 1983 .

[59]  Douglas A. Reynolds,et al.  Speaker Verification Using Adapted Gaussian Mixture Models , 2000, Digit. Signal Process..

[60]  Bo Zhao,et al.  Probabilistic topic models with biased propagation on heterogeneous information networks , 2011, KDD.

[61]  David M. Blei,et al.  Supervised Topic Models , 2007, NIPS.

[62]  Jordi Vitrià,et al.  Learning mixture models using a genetic version of the EM algorithm , 2000, Pattern Recognition Letters.

[63]  Deng Cai,et al.  Topic modeling with network regularization , 2008, WWW.

[64]  David G. Kleinbaum,et al.  Maximum Likelihood Techniques: An Overview , 1994 .

[65]  Miguel Á. Carreira-Perpiñán,et al.  Practical Identifiability of Finite Mixtures of Multivariate Bernoulli Distributions , 2000, Neural Computation.

[66]  Naonori Ueda,et al.  Deterministic annealing EM algorithm , 1998, Neural Networks.

[67]  Padhraic Smyth,et al.  Model selection for probabilistic clustering using cross-validated likelihood , 2000, Stat. Comput..

[68]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[69]  Michael I. Jordan,et al.  Graphical Models, Exponential Families, and Variational Inference , 2008, Found. Trends Mach. Learn..

[70]  Thomas L. Griffiths,et al.  Probabilistic author-topic models for information discovery , 2004, KDD.

[71]  Thomas Hofmann,et al.  Unsupervised Learning by Probabilistic Latent Semantic Analysis , 2004, Machine Learning.

[72]  Mark Steyvers,et al.  Finding scientific topics , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[73]  Gilles Celeux,et al.  A Component-Wise EM Algorithm for Mixtures , 2001, 1201.5913.

[74]  W. Eric L. Grimson,et al.  Adaptive background mixture models for real-time tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[75]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[76]  C. S. Wallace,et al.  Unsupervised Learning Using MML , 1996, ICML.

[77]  Djamel Bouchaffra,et al.  Genetic-based EM algorithm for learning Gaussian mixture models , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[78]  William D. Penny,et al.  Bayesian Approaches to Gaussian Mixture Modeling , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[79]  Geoffrey J. McLachlan,et al.  Finite Mixture Models , 2019, Annual Review of Statistics and Its Application.