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Mixtures of probabilistic principal component analysis (MPPCA) is awell-known mixture model extension of principal component analysis (PCA).Similar to PCA, MPPCA assumes the data samples in each mixture containhomoscedastic noise. However, datasets with heterogeneous noise across samplesare becoming increasingly common, as larger datasets are generated bycollecting samples from several sources with varying noise profiles. Theperformance of MPPCA is suboptimal for data with heteroscedastic noise acrosssamples. This paper proposes a heteroscedastic mixtures of probabilistic PCAtechnique (HeMPPCAT) that uses a generalized expectation-maximization (GEM)algorithm to jointly estimate the unknown underlying factors, means, and noisevariances under a heteroscedastic noise setting. Simulation results illustratethe improved factor estimates and clustering accuracies of HeMPPCAT compared toMPPCA.

HeMPPCAT: Mixtures of Probabilistic Principal Component Analysers for Data with Heteroscedastic Noise