Structured Convex Optimization Method for Orthogonal Nonnegative Matrix Factorization

Junjun Pan, Kwok Po NG, Xiongjun Zhang

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

Orthogonal nonnegative matrix factorization plays an important role for data clustering and machine learning. In this paper, we propose a new optimization model for orthogonal nonnegative matrix factorization based on the structural properties of orthogonal nonnegative matrix. The new model can be solved by a novel convex relaxation technique which can be employed quite efficiently. Numerical examples in document clustering, image segmentation and hyperspectral unmixing are used to test the performance of the proposed model. The performance of our method is better than the other testing methods in terms of clustering accuracy.

Original languageEnglish
Title of host publication2018 24th International Conference on Pattern Recognition, ICPR 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages459-464
Number of pages6
ISBN (Electronic)9781538637883
DOIs
Publication statusPublished - 26 Nov 2018
Event24th International Conference on Pattern Recognition, ICPR 2018 - Beijing, China
Duration: 20 Aug 201824 Aug 2018

Publication series

NameProceedings - International Conference on Pattern Recognition
Volume2018-August
ISSN (Print)1051-4651

Conference

Conference24th International Conference on Pattern Recognition, ICPR 2018
Country/TerritoryChina
CityBeijing
Period20/08/1824/08/18

Scopus Subject Areas

  • Computer Vision and Pattern Recognition

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