Sparse two-dimensional singular value decomposition

Junhui Hou, Jie Chen, Lap Pui Chau, Ying He

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)


In this paper, we propose a new data-driven transform, called sparse two-dimensional singular value decomposition (S2DSVD). By leveraging the advantages of discrete cosine transform and the conventional 2D SVD, we decompose a set of matrices into transform coefficient matrices with sparse and orthogonal basis functions. Such sparsity characteristic can significantly reduce their overhead, hence being beneficial to data compression. We formulate S2DSVD as a constrained optimization problem and solve it via alternative iteration. We demonstrate the efficacy of S2DSVD on image and video datasets, and observe that it can produce results with error comparable to 2D SVD whereas its space complexity is significantly smaller than 2D SVD.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Multimedia and Expo, ICME 2016
PublisherIEEE Computer Society
ISBN (Electronic)9781467372589
Publication statusPublished - 25 Aug 2016
Externally publishedYes
Event2016 IEEE International Conference on Multimedia and Expo, ICME 2016 - Seattle, United States
Duration: 11 Jul 201615 Jul 2016

Publication series

NameProceedings - IEEE International Conference on Multimedia and Expo
ISSN (Print)1945-7871
ISSN (Electronic)1945-788X


Conference2016 IEEE International Conference on Multimedia and Expo, ICME 2016
Country/TerritoryUnited States


  • data compression
  • decorrelation
  • optimization
  • singular value decomposition

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications


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