Generalized Principal Component Analysis
arXiv:1907.02647v187 citations
Originality Synthesis-oriented
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This is an incremental extension of PCA for specialized data types in statistics and machine learning.
The paper tackles dimension reduction for non-normally distributed data by introducing GLM-PCA, providing derivations, covariate integration, and interpretability improvements.
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.