Generalized Principal Component Analysis
Machine Learning
2019-07-08 v1 Machine Learning
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.
Keywords
Cite
@article{arxiv.1907.02647,
title = {Generalized Principal Component Analysis},
author = {F. William Townes},
journal= {arXiv preprint arXiv:1907.02647},
year = {2019}
}