An information theoretic analysis of maximum likelihood mixture estimation for exponential families
暂无分享,去创建一个
Inderjit S. Dhillon | Joydeep Ghosh | Arindam Banerjee | Srujana Merugu | I. Dhillon | A. Banerjee | Joydeep Ghosh | S. Merugu
[1] Toby Berger,et al. Rate distortion theory : a mathematical basis for data compression , 1971 .
[2] 丸山 徹. Convex Analysisの二,三の進展について , 1977 .
[3] William A. Pearlman,et al. Optimal encoding of discrete-time continuous-amplitude memoryless sources with finite output alphabets , 1980, IEEE Trans. Inf. Theory.
[4] R. Redner,et al. Mixture densities, maximum likelihood, and the EM algorithm , 1984 .
[5] Thomas M. Cover,et al. Elements of Information Theory , 2005 .
[6] Kenneth Rose,et al. A mapping approach to rate-distortion computation and analysis , 1994, IEEE Trans. Inf. Theory.
[7] Yishay Mansour,et al. An Information-Theoretic Analysis of Hard and Soft Assignment Methods for Clustering , 1997, UAI.
[8] K. Rose. Deterministic annealing for clustering, compression, classification, regression, and related optimization problems , 1998, Proc. IEEE.
[9] Geoffrey E. Hinton,et al. A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants , 1998, Learning in Graphical Models.
[10] Manfred K. Warmuth,et al. Relative Expected Instantaneous Loss Bounds , 2000, J. Comput. Syst. Sci..
[11] Naftali Tishby,et al. The information bottleneck method , 2000, ArXiv.
[12] Sanjoy Dasgupta,et al. A Generalization of Principal Components Analysis to the Exponential Family , 2001, NIPS.
[13] Tom M. Mitchell,et al. Using unlabeled data to improve text classification , 2001 .
[14] Noam Slonim,et al. Maximum Likelihood and the Information Bottleneck , 2002, NIPS.
[15] Naftali Tishby,et al. An Information Theoretic Tradeoff between Complexity and Accuracy , 2003, COLT.
[16] Inderjit S. Dhillon,et al. A Divisive Information-Theoretic Feature Clustering Algorithm for Text Classification , 2003, J. Mach. Learn. Res..
[17] Manfred K. Warmuth,et al. Relative Loss Bounds for On-Line Density Estimation with the Exponential Family of Distributions , 1999, Machine Learning.
[18] Inderjit S. Dhillon,et al. Clustering with Bregman Divergences , 2005, J. Mach. Learn. Res..