Fast Clustering with Co-Clustering Via Discrete Non-Negative Matrix Factorization for Image Identification

Feiping Nie, Shenfei Pei, Rong Wang, Xuelong Li

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

13 Scopus citations

Abstract

How to effectively cluster large-scale image data sets is a challenge and is receiving more and more attention. To address this problem, a novel clustering method called fast clustering with co-clustering via discrete non-negative matrix factorization, is proposed. Inspired by co-clustering, our algorithm reduces computational complexity by transforming clustering tasks into co-clustering tasks. Although our model has the same form of objective function as normalized cut, we relax it to a matrix decomposition problem, which is different from most graph-based approaches. In addition, an efficient optimization algorithm is proposed to solve the relaxed problem, where a discrete solution corresponding to the clustering result can be directly obtained. Extensive experiments have been conducted on several synthetic data sets and real word data sets. Compared with the state-of-the-art clustering methods, the proposed algorithm achieves very promising performance.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2073-2077
Number of pages5
ISBN (Electronic)9781509066315
DOIs
StatePublished - May 2020
Event2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Barcelona, Spain
Duration: 4 May 20208 May 2020

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2020-May
ISSN (Print)1520-6149

Conference

Conference2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
Country/TerritorySpain
CityBarcelona
Period4/05/208/05/20

Keywords

  • clustering
  • co-clustering
  • Fast
  • non-negative matrix factorization

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