Title :
Links between PPCA and subspace methods for complete Gaussian density estimation
Author :
Chong Wang ; Wenyuan Wang
Author_Institution :
Dept. of Autom., Tsinghua Univ., Beijing
fDate :
5/1/2006 12:00:00 AM
Abstract :
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
Keywords :
Gaussian processes; covariance matrices; eigenvalues and eigenfunctions; estimation theory; learning (artificial intelligence); pattern recognition; principal component analysis; complete high-dimensional Gaussian density estimation; covariance matrix; eigenspace decomposition; machine learning; pattern recognition; probabilistic principal component analysis; robust estimation; subspace methods; Automation; Covariance matrix; Eigenvalues and eigenfunctions; Gaussian noise; Information processing; Machine learning; Matrix decomposition; Noise robustness; Pattern recognition; Principal component analysis; Complete Gaussian density estimation; eigenspace decomposition; probabilistic principal component analysis (PPCA); subspace method; Algorithms; Artificial Intelligence; Computer Simulation; Information Storage and Retrieval; Models, Statistical; Neural Networks (Computer); Normal Distribution; Pattern Recognition, Automated; Principal Component Analysis; Systems Theory;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2006.871718
