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- W2337871617 abstract "Multivariate Gaussian distribution is a multi-dimensional generalized model for conventional pervasive Gaussian distribution and Gaussian mixture model (GMM) is a practical model widely used in clustering analysis. In this paper, we aim at clustering the Gaussian mixture components into reduced number of components, thus simplifying the GMM. By utilizing a class of Bregman divergences in information-theoretic context, we minimize the in-cluster relative entropy, and derived a closed-form of optimal parameters by divergence factorization. Symmetric relative entropy is used to avoid the asymmetric manner of divergence. Our algorithm is time-saving as a simplification of GMM and demonstrates its superiority visually in practical clustering-based image segmentation problem, in comparison with conventional methods, such as k-means and GMM-EM." @default.
- W2337871617 created "2016-06-24" @default.
- W2337871617 creator A5003642180 @default.
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- W2337871617 date "2013-01-01" @default.
- W2337871617 modified "2023-09-23" @default.
- W2337871617 title "Information-Theoretic Clustering for Gaussian Mixture Model via Divergence Factorization" @default.
- W2337871617 cites W1548595933 @default.
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- W2337871617 doi "https://doi.org/10.1007/978-3-642-38466-0_63" @default.
- W2337871617 hasPublicationYear "2013" @default.
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