A Fast Distributed Non-Negative Matrix Factorization Algorithm Based on DSGD

In numerous solutions, the distributed stochastic gradient descent algorithm is one of the most popular algorithms for parallelization matrix decomposition. However, in parallel calculation, the computing speed of each computing node was greatly different because of the imbalance of the computing no...

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Bibliographic Details
Published in:International journal of distributed systems and technologies Vol. 9; no. 3; pp. 24 - 38
Main Authors: Chen, Baifan, Gao, Yan, Zhou, Lingjun, Xing, Xiaobing
Format: Journal Article
Language:English
Published: Hershey IGI Global 01.07.2018
Subjects:
Algorithms
Computation
Control algorithms
Convergence
Decomposition
Efficiency
Nodes
Parallel processing
Recommender systems
Scheduling
Variables
China
ISSN:1947-3532, 1947-3540
Online Access:Get full text
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Summary:In numerous solutions, the distributed stochastic gradient descent algorithm is one of the most popular algorithms for parallelization matrix decomposition. However, in parallel calculation, the computing speed of each computing node was greatly different because of the imbalance of the computing nodes. This article reduced the data skew for all computing nodes during distributed execution to solve the problem of locking waiting. The improved algorithm on DSGD was named D-DSGD, which reduced the time consumption of the algorithm and improved the utilization rate of the nodes. Meanwhile, the dynamic step size adjusting strategy was applied to improve the convergence rate of the algorithm. To ensure the non-negative matrix decomposition, non-negative control was added into D-DSGD and the improved algorithm was named D-NMF. Compared with the existing methods, the proposed algorithm in this article has a marked impact on reducing the latency and speed of convergence.
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ISSN:1947-3532
1947-3540
DOI:10.4018/IJDST.2018070102