DID: Distributed Incremental Block Coordinate Descent for Nonnegative Matrix Factorization

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2018-04-29
Authors
Gao, Tianxiang
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Association for the Advancement of Artificial Intelligence
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Electrical and Computer Engineering
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 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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This is a manuscript of a proceeding published as Gao, Tianxiang, and Chris Chu. "DID: distributed incremental block coordinate descent for nonnegative matrix factorization." In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence and Thirtieth Innovative Applications of Artificial Intelligence Conference and Eighth AAAI Symposium on Educational Advances in Artificial Intelligence, pp. 2991-2998. 2018. DOI: 10.1609/aaai.v32i1.11736. Copyright 2018 Association for the Advancement of Artificial Intelligence. Posted with permission.
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