期刊
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
卷 22, 期 11, 页码 1318-1323出版社
IEEE COMPUTER SOC
DOI: 10.1109/34.888716
关键词
Gaussian mixtures; maximization algorithms; mode finding; bump finding; error bars; sparseness
Gradient-quadratic and fixed-point Iteration algorithms and appropriate Values for their control parameters are derived for finding all modes of a Gaussian mixture, a problem with applications in clustering and regression. The significance of the modes found is quantified locally by Hessian-based error bars and globally by the entropy as sparseness measure.
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