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arXiv · 1802.08938

DID: Distributed Incremental Block Coordinate Descent for Nonnegative Matrix Factorization

Tianxiang Gao·Chris Chu
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Abstract

Nonnegative matrix factorization (NMF) has attracted much attention in the last decade as a dimension reduction method in many applications. Due to the explosion in the size of data, naturally the samples are collected and stored distributively in local computational nodes. Thus, there is a growing need to develop algorithms in a distributed memory architecture. We propose a novel distributed algorithm, called \textit{distributed incremental block coordinate descent} (DID), to solve the problem. By adapting the block coordinate descent framework, closed-form update rules are obtained in DID. Moreover, DID performs updates incrementally based on the most recently updated residual matrix. As a result, only one communication step per iteration is required. The correctness, efficiency, and scalability of the proposed algorithm are verified in a series of numerical experiments.

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Tianxiang Gao, Chris Chu. 2018-02-25. DID: Distributed Incremental Block Coordinate Descent for Nonnegative Matrix Factorization. https://arxiv.org/abs/1802.08938

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