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Links between PPCA and subspace methods for complete Gaussian density estimation

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2 Author(s)
Chong Wang ; Dept. of Autom., Tsinghua Univ., Beijing ; Wenyuan Wang

High-dimensional density estimation is a fundamental problem in pattern recognition and machine learning areas. In this letter, we show that, for complete high-dimensional Gaussian density estimation, two widely used methods, probabilistic principal component analysis and a typical subspace method using eigenspace decomposition, actually give the same results. Additionally, we present a unified view from the aspect of robust estimation of the covariance matrix

Published in:

Neural Networks, IEEE Transactions on  (Volume:17 ,  Issue: 3 )

Date of Publication:

May 2006

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