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arXiv · 1907.00139

Fast Convolutive Nonnegative Matrix Factorization Through Coordinate and Block Coordinate Updates

Anthony Degleris·Ben Antin·Surya Ganguli·Alex H Williams
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Abstract

Identifying recurring patterns in high-dimensional time series data is an important problem in many scientific domains. A popular model to achieve this is convolutive nonnegative matrix factorization (CNMF), which extends classic nonnegative matrix factorization (NMF) to extract short-lived temporal motifs from a long time series. Prior work has typically fit this model by multiplicative parameter updates---an approach widely considered to be suboptimal for NMF, especially in large-scale data applications. Here, we describe how to extend two popular and computationally scalable NMF algorithms---Hierarchical Alternating Least Squares (HALS) and Alternatining Nonnegative Least Squares (ANLS)---for the CNMF model. Both methods demonstrate performance advantages over multiplicative updates on large-scale synthetic and real world data.

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Anthony Degleris, Ben Antin, Surya Ganguli, Alex H Williams. 2019-06-29. Fast Convolutive Nonnegative Matrix Factorization Through Coordinate and Block Coordinate Updates. https://arxiv.org/abs/1907.00139

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