Sparse two-dimensional singular value decomposition

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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Author(s)

Detail(s)

Original languageEnglish
Title of host publicationProceedings - IEEE International Conference on Multimedia and Expo
PublisherIEEE Computer Society
Volume2016-August
ISBN (Print)9781467372589
Publication statusPublished - 25 Aug 2016
Externally publishedYes

Publication series

Name
Volume2016-August
ISSN (Print)1945-7871
ISSN (Electronic)1945-788X

Conference

Title2016 IEEE International Conference on Multimedia and Expo, ICME 2016
PlaceUnited States
CitySeattle
Period11 - 15 July 2016

Abstract

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.

Research Area(s)

  • data compression, decorrelation, optimization, singular value decomposition

Citation Format(s)

Sparse two-dimensional singular value decomposition. / Hou, Junhui; Chen, Jie; Chau, Lap-Pui; He, Ying.

Proceedings - IEEE International Conference on Multimedia and Expo. Vol. 2016-August IEEE Computer Society, 2016. 7552922.

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review