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Efficient Multiple Kernel Learning Algorithms Using Low-Rank Representation
Joint Authors
Zu, Baokai
Xia, Kewen
Niu, Wenjia
Bai, Jianchuan
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-08-22
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
Unlike Support Vector Machine (SVM), Multiple Kernel Learning (MKL) allows datasets to be free to choose the useful kernels based on their distribution characteristics rather than a precise one.
It has been shown in the literature that MKL holds superior recognition accuracy compared with SVM, however, at the expense of time consuming computations.
This creates analytical and computational difficulties in solving MKL algorithms.
To overcome this issue, we first develop a novel kernel approximation approach for MKL and then propose an efficient Low-Rank MKL (LR-MKL) algorithm by using the Low-Rank Representation (LRR).
It is well-acknowledged that LRR can reduce dimension while retaining the data features under a global low-rank constraint.
Furthermore, we redesign the binary-class MKL as the multiclass MKL based on pairwise strategy.
Finally, the recognition effect and efficiency of LR-MKL are verified on the datasets Yale, ORL, LSVT, and Digit.
Experimental results show that the proposed LR-MKL algorithm is an efficient kernel weights allocation method in MKL and boosts the performance of MKL largely.
American Psychological Association (APA)
Niu, Wenjia& Xia, Kewen& Zu, Baokai& Bai, Jianchuan. 2017. Efficient Multiple Kernel Learning Algorithms Using Low-Rank Representation. Computational Intelligence and Neuroscience،Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1140913
Modern Language Association (MLA)
Niu, Wenjia…[et al.]. Efficient Multiple Kernel Learning Algorithms Using Low-Rank Representation. Computational Intelligence and Neuroscience No. 2017 (2017), pp.1-9.
https://search.emarefa.net/detail/BIM-1140913
American Medical Association (AMA)
Niu, Wenjia& Xia, Kewen& Zu, Baokai& Bai, Jianchuan. Efficient Multiple Kernel Learning Algorithms Using Low-Rank Representation. Computational Intelligence and Neuroscience. 2017. Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1140913
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1140913