research-article

DSANLS: Accelerating Distributed Nonnegative Matrix Factorization via Sketching

Authors:

Nikos Mamoulis,

David W. CheungAuthors Info & Claims

WSDM '18: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining

Pages 450 - 458

https://doi.org/10.1145/3159652.3159662

Published: 02 February 2018 Publication History

Abstract

Nonnegative matrix factorization (NMF) has been successfully applied in different fields, such as text mining, image processing, and video analysis. NMF is the problem of determining two nonnegative low rank matrices U and V, for a given input matrix M, such that m ≈ UV⊥. There is an increasing interest in parallel and distributed NMF algorithms, due to the high cost of centralized NMF on large matrices. In this paper, we propose a distributed sketched alternating nonnegative least squares(DSANLS) framework for NMF, which utilizes a matrix sketching technique to reduce the size of nonnegative least squares subproblems in each iteration for U and V. We design and analyze two different random matrix generation techniques and two subproblem solvers. Our theoretical analysis shows that DSANLS converges to the stationary point of the original NMF problem and it greatly reduces the computational cost in each subproblem as well as the communication cost within the cluster. DSANLS is implemented using MPI for communication, and tested on both dense and sparse real datasets. The results demonstrate the efficiency and scalability of our framework, compared to the state-of-art distributed NMF MPI implementation.

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Cited By

Qian YTan CDing DLi HMamoulis N(2022)Fast and Secure Distributed Nonnegative Matrix FactorizationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.298596434:2(653-666)Online publication date: 1-Feb-2022
https://doi.org/10.1109/TKDE.2020.2985964
Han ROsting BWang DXu Y(2022)Probabilistic methods for approximate archetypal analysisInformation and Inference: A Journal of the IMA10.1093/imaiai/iaac00812:1(466-493)Online publication date: 12-May-2022
https://doi.org/10.1093/imaiai/iaac008

Index Terms

DSANLS: Accelerating Distributed Nonnegative Matrix Factorization via Sketching
1. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

WSDM '18: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining

February 2018

821 pages

ISBN:9781450355810

DOI:10.1145/3159652

General Chairs:
Yi Chang
Jilin University, Huawei Inc.
,
Chengxiang Zhai
University of Illinois Urbana-Champaign
,
Program Chairs:
Yan Liu
University of Southern California
,
Yoelle Maarek
Amazon

Copyright © 2018 ACM.

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Published: 02 February 2018

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European Union's Horizon 2020 research and innovation programme
Hong Kong Research Grant Council

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WSDM 2018

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WSDM 2018: The Eleventh ACM International Conference on Web Search and Data Mining

February 5 - 9, 2018

CA, Marina Del Rey, USA

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WSDM '18 Paper Acceptance Rate 81 of 514 submissions, 16%;

Overall Acceptance Rate 498 of 2,863 submissions, 17%

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Cited By

Qian YTan CDing DLi HMamoulis N(2022)Fast and Secure Distributed Nonnegative Matrix FactorizationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.298596434:2(653-666)Online publication date: 1-Feb-2022
https://doi.org/10.1109/TKDE.2020.2985964
Han ROsting BWang DXu Y(2022)Probabilistic methods for approximate archetypal analysisInformation and Inference: A Journal of the IMA10.1093/imaiai/iaac00812:1(466-493)Online publication date: 12-May-2022
https://doi.org/10.1093/imaiai/iaac008

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