Probabilistic Models for Clustering
暂无分享,去创建一个
[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.