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
Author(s)
Detail(s)
Original language | English |
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Title of host publication | Proceedings - IEEE International Conference on Multimedia and Expo |
Publisher | IEEE Computer Society |
Volume | 2016-August |
ISBN (Print) | 9781467372589 |
Publication status | Published - 25 Aug 2016 |
Externally published | Yes |
Publication series
Name | |
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Volume | 2016-August |
ISSN (Print) | 1945-7871 |
ISSN (Electronic) | 1945-788X |
Conference
Title | 2016 IEEE International Conference on Multimedia and Expo, ICME 2016 |
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Place | United States |
City | Seattle |
Period | 11 - 15 July 2016 |
Link(s)
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 et al.
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