MAP approximation to the variational Bayes Gaussian mixture model and application
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[1] P. Fua,et al. Pose estimation for category specific multiview object localization , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.
[2] Nizar Bouguila,et al. Variational Bayesian inference for infinite generalized inverted Dirichlet mixtures with feature selection and its application to clustering , 2015, Applied Intelligence.
[3] Arne Leijon,et al. Bayesian Estimation of Beta Mixture Models with Variational Inference , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[4] Han Wang,et al. Sparse Coding Based Fisher Vector Using a Bayesian Approach , 2017, IEEE Signal Processing Letters.
[5] Radford M. Neal. Pattern Recognition and Machine Learning , 2007, Technometrics.
[6] Marc Sebban,et al. Supervised learning of Gaussian mixture models for visual vocabulary generation , 2012, Pattern Recognit..
[7] Cordelia Schmid,et al. Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).
[8] Radford M. Neal. Markov Chain Sampling Methods for Dirichlet Process Mixture Models , 2000 .
[9] Changhu Wang,et al. Probabilistic models for supervised dictionary learning , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[10] Chong Wang,et al. Nested Hierarchical Dirichlet Processes , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[11] Max Welling,et al. Bayesian k-Means as a Maximization-Expectation Algorithm , 2009, Neural Computation.
[12] David J. C. MacKay,et al. Information Theory, Inference, and Learning Algorithms , 2004, IEEE Transactions on Information Theory.
[13] Han Wang,et al. Learning a field of Gaussian mixture model for image classification , 2016, 2016 14th International Conference on Control, Automation, Robotics and Vision (ICARCV).
[14] Yee Whye Teh,et al. Sharing Clusters among Related Groups: Hierarchical Dirichlet Processes , 2004, NIPS.
[15] Han Wang,et al. Learning Gaussian mixture model with a maximization-maximization algorithm for image classification , 2016, 2016 12th IEEE International Conference on Control and Automation (ICCA).
[16] Michael I. Jordan,et al. Variational inference for Dirichlet process mixtures , 2006 .
[17] Pietro Perona,et al. Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.
[18] Adrian Corduneanu,et al. Variational Bayesian Model Selection for Mixture Distributions , 2001 .
[19] Cordelia Schmid,et al. Approximate Fisher Kernels of Non-iid Image Models for Image Categorization , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[20] Lei Wang,et al. Encoding High Dimensional Local Features by Sparse Coding Based Fisher Vectors , 2014, NIPS.
[21] Nizar Bouguila,et al. Variational learning of a Dirichlet process of generalized Dirichlet distributions for simultaneous clustering and feature selection , 2013, Pattern Recognit..