GUANGTUN BEN ZHU, PhD

Nonnegative Matrix Factorization (NMF) and PCA

 

I have published my Python implementation on GitHub and PyPI (You can install it via pip)

https://github.com/guangtunbenzhu/NonnegMFPy 

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You can find the reference here: https://arxiv.org/abs/1612.06037

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Dimensionality reduction and matrix factorization techniques have many applications in physics and astronomy. In astronomy, a particularly useful technique is nonnegative matrix factorization, since the flux of an astronomical source does not go negative.

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I have developed a code of NMF that can take into account of heteroscedastic uncertainties and missing data (while standard PCA can't). The algorithm was originally developed by Sam Roweis & Mike Blanton (2007), who vastly improved the simple and elegant update rules discovered by Lee & Seung (2001). I have vectorized the algorithm, which can be easily implemented in any modern vector language.