Links Between PPCA and Subspace Methods for Complete Gaussian Density Estimation  

Oleh:

Wang, Chong ; Wang, Wenyuan

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 17 no. 3 (May 2006), page 789-791.
Topik: gaussian; PPCA; subspace methods; complete gaussian; density estimation

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

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.

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