Generalized Principal Component Analysis

2019/07/03 by F. William Townes, Townes, F. William
Chemistry · Computer Science · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural Networks and Applications #Spectroscopy and Chemometric Analyses #Time Series Analysis and Forecasting

paper · pdf · doi:10.48550/arxiv.1907.02647

openalex publication_date 2019/07/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Generalized principal component analysis (GLM-PCA) facilitates dimension reduction of non-normally distributed data. We provide a detailed derivation of GLM-PCA with a focus on optimization. We also demonstrate how to incorporate covariates, and suggest post-processing transformations to improve interpretability of latent factors.

Citations

• Analysis of a complex of statistical variables into principal components.

Related

• Principal Component Analysis When n < p: Challenges and Solutions
• GT-PCA: Effective and Interpretable Dimensionality Reduction with General Transform-Invariant Principal Component Analysis
• Principal Component Analysis
• Supervised Linear Dimension-Reduction Methods: Review, Extensions, and Comparisons
• Principal component analysis for big data