Supervised Quantile Normalization for Low-rank Matrix Approximation

8 Feb 2020Marco CuturiOlivier TeboulJonathan Niles-WeedJean-Philippe Vert

Low rank matrix factorization is a fundamental building block in machine learning, used for instance to summarize gene expression profile data or word-document counts. To be robust to outliers and differences in scale across features, a matrix factorization step is usually preceded by ad-hoc feature normalization steps, such as \texttt{tf-idf} scaling or data whitening... (read more)

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