Probabilistic Principal Component Analysis using Expectation Maximization (PPCA-EM) for Analyzing 3D Volumes with Missing Data

Statistical analysis of neural data: The expectation-maximization (EM) algorithm

Expectation-Maximization (EM) algorithm

Modelling Handwitten Digit Data using Probabilistic Principal Component Analysis

Principal Component Analysis (PCA) is one century old now. Nevertheless, it still undergoes research and new extensions are found. Probabilistic Principal Component Analysis (PPCA, proposed by Tipping and Bishop) is one of these recent PCA extensions. PPCA defines a probabilistic generative model for PCA.It can easily be extended to mixture models. Among recent mixture density theoretical devel...

The Expectation Maximization (EM) algorithm

In the previous class we already mentioned that many of the most powerful probabilistic models contain hidden variables. We will denote these variables with y. It is usually also the case that these models are most easily written in terms of their joint density, p(d,y,θ) = p(d|y,θ) p(y|θ) p(θ) (1) Remember also that the objective function we want to maximize is the log-likelihood (possibly incl...

Probabilistic Principal Component Analysis

Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/page/info/about/policies/terms.jsp. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive o...