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[ascl:2112.023] wpca: Weighted Principal Component Analysis in Python

wpca, written in Python, offers several implementations of Weighted Principal Component Analysis and uses an interface similar to scikit-learn'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 expectation-maximization approach to solve simultaneously for the principal vectors and principal components of weighted data. It also includes a standard non-weighted PCA implemented using the singular value decomposition, primarily to be useful for testing.

Code site:
https://github.com/jakevdp/wpca
Used in:
https://ui.adsabs.harvard.edu/abs/2021A%26A...653A..43C
Bibcode:
2021ascl.soft12023V

Views: 483

ascl:2112.023
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