Nonconvex alternating direction method of multipliers for distributed sparse principal component analysis

Davood Hajinezhad, Mingyi Hong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Scopus citations

Abstract

In this paper, we propose distributed algorithms to perform sparse principal component analysis (SPCA). The key benefit of the proposed algorithms is their ability to handle distributed data sets. Our algorithms are able to handle a few sparse-promoting regularizers (i.e., the convex norm and the nonconvex log-sum penalty) as well as different forms of data partition (i.e., partition across rows or columns of the data matrix). Our methods are based on a nonconvex ADMM framework, and they are shown to converge to stationary solutions of various nonconvex SPCA formulations. Numerical experiments based on both real and synthetic data sets, conducted on high performance computing (HPC) clusters, demonstrate the effectiveness of our approaches.

Original languageEnglish (US)
Title of host publication2015 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages255-259
Number of pages5
ISBN (Electronic)9781479975914
DOIs
StatePublished - Feb 23 2016
EventIEEE Global Conference on Signal and Information Processing, GlobalSIP 2015 - Orlando, United States
Duration: Dec 13 2015Dec 16 2015

Publication series

Name2015 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015

Other

OtherIEEE Global Conference on Signal and Information Processing, GlobalSIP 2015
CountryUnited States
CityOrlando
Period12/13/1512/16/15

Keywords

  • Distributed Optimization
  • Non-Convex ADMM
  • Sparse PCA

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