wpca: Weighted Principal Component Analysis in Python
Abstract
wpca, written in Python, offers several implementations of Weighted Principal Component Analysis and uses an interface similar to scikitlearn's sklearn.decomposition.PCA. Implementations include a direct decomposition of a weighted covariance matrix to compute principal vectors, and then a weighted least squares optimization to compute principal components, and an iterative expectationmaximization approach to solve simultaneously for the principal vectors and principal components of weighted data. It also includes a standard nonweighted PCA implemented using the singular value decomposition, primarily to be useful for testing.
 Publication:

Astrophysics Source Code Library
 Pub Date:
 December 2021
 Bibcode:
 2021ascl.soft12023V
 Keywords:

 Software