residual Component Analysis: Generalizing PCA for more flexible inference in linear-Gaussian models: Difference between revisions
Jump to navigation
Jump to search
HanShengSun (talk | contribs) (Created page with "==Introduction== Probabilistic principle component analysis (PPCA) decomposes the covariance of a data vector <math>y</math> in <math>R^p</math>, into a low-rank term and a spher...") |
HanShengSun (talk | contribs) |
||
| Line 1: | Line 1: | ||
==Introduction== | ==Introduction== | ||
Probabilistic principle component analysis (PPCA) decomposes the covariance of a data vector <math>y</math> in <math>R^p</math>, into a low-rank term and a spherical noise term. <center><math>y \sim N(0, WW^T+\sigma I )</math></center> | Probabilistic principle component analysis (PPCA) decomposes the covariance of a data vector <math> y</math> in <math>\mathbb{R}^p</math>, into a low-rank term and a spherical noise term. <center><math>y \sim \mathcal{N} (0, WW^T+\sigma I )</math></center> | ||
Revision as of 17:12, 3 July 2013
Introduction
Probabilistic principle component analysis (PPCA) decomposes the covariance of a data vector [math]\displaystyle{ y }[/math] in [math]\displaystyle{ \mathbb{R}^p }[/math], into a low-rank term and a spherical noise term.