2023/01/21 by Alec S. Xu, Laura Balzano, Xu, Alec S. +3
Chemistry · Computer Science · Engineering · #Advanced Chemical Sensor Technologies #Bayesian Methods and Mixture Models #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (stat.ML) #Methodology (stat.ME) #Signal Processing (eess.SP) #Spectroscopy and Chemometric Analyses #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2301.08852
openalex publication_date 2023/01/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Mixtures of probabilistic principal component analysis (MPPCA) is a well-known mixture model extension of principal component analysis (PCA). Similar to PCA, MPPCA assumes the data samples in each mixture contain homoscedastic noise. However, datasets with heterogeneous noise across samples are becoming increasingly common, as larger datasets are generated by collecting samples from several sources with varying noise profiles. The performance of MPPCA is suboptimal for data with heteroscedastic noise across samples. This paper proposes a heteroscedastic mixtures of probabilistic PCA technique (HeMPPCAT) that uses a generalized expectation-maximization (GEM) algorithm to jointly estimate the unknown underlying factors, means, and noise variances under a heteroscedastic noise setting. Simulation results illustrate the improved factor estimates and clustering accuracies of HeMPPCAT compared to MPPCA.