Using Algebraic Factorizations for Interpretable Learning

Deanna Needell
University of California, Los Angeles (UCLA)

Non-negative Matrix Factorization (NMF) is a fundamental tool for dictionary learning problems, giving an approximate representation of complex data sets in terms of a reduced number of extracted features. In this talk, we will introduce the main concept of NMF, its implementation, and its online and streaming variations. We will showcase how mathematical tools like this can be used for interpretable learning tasks. These applications range from imaging and medicine to forecasting and collaborative filtering. Discussion and questions are welcome.

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