title: Links between PPCA and subspace methods for complete Gaussian density estimation.
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.
authors.forename: Chong
authors.initials: C
authors.lastname: Wang
authors.forename: Wenyuan
authors.initials: W
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D016247:Information Storage and Retrieval / Q000379:methods*; D015233:Models, Statistical; D016571:Neural Networks, Computer; D016011:Normal Distribution; D010363:Pattern Recognition, Automated / Q000379:methods*; D025341:Principal Component Analysis; D013598:Systems Theory