A well-defined mixture model for probabilistic principal component analyzers, whose parameters can be determined using an expectation-maximization algorithm.

Jun 26, 2006 · The probabilistic PCA algorithm was obtained by introducing a constraint into the noise matrix of the factor analysis latent variable model.

Jan 21, 2023 · This paper proposes a heteroscedastic mixtures of probabilistic PCA technique (HeMPPCAT) that uses a generalized expectation-maximization (GEM) ...

Mixtures of probabilistic principal component analyzers model high-dimensional nonlinear data by combining local linear models. Each mixture component is ...

This chapter contains sections titled: Introduction, Latent Variable Models and PCA, Probabilistic PCA, Mixtures of Probabilistic Principal Component Analyzers,

Jan 25, 2023 · Mixtures of probabilistic principal component analysis. (MPPCA) is a well-known mixture model extension of principal component analysis ...

3 Mixtures of Robust PPCAs. A mixture of M robust probabilistic principal component analyzers is defined as p(yn)= Pm πmp(yn|m). (4) where {πm}M m=1 is the ...

We then introduce probabilistic principal component analysis (PPCA) in section 3, showing how the principal subspace of a set of data vectors can be obtained.

Mixture of probabilistic principal component analyzers (MPPCA) has been used for modeling non-Gaussian process data and monitoring in the past.