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Efficient Multiple Kernel Learning Algorithms Using Low-Rank Representation

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, howev...

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Detalles Bibliográficos
Autores principales: Niu, Wenjia, Xia, Kewen, Zu, Baokai, Bai, Jianchuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5585640/
https://www.ncbi.nlm.nih.gov/pubmed/28912801
http://dx.doi.org/10.1155/2017/3678487
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author Niu, Wenjia
Xia, Kewen
Zu, Baokai
Bai, Jianchuan
author_facet Niu, Wenjia
Xia, Kewen
Zu, Baokai
Bai, Jianchuan
author_sort Niu, Wenjia
collection PubMed
description 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.
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spelling pubmed-55856402017-09-14 Efficient Multiple Kernel Learning Algorithms Using Low-Rank Representation Niu, Wenjia Xia, Kewen Zu, Baokai Bai, Jianchuan Comput Intell Neurosci Research Article 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. Hindawi 2017 2017-08-22 /pmc/articles/PMC5585640/ /pubmed/28912801 http://dx.doi.org/10.1155/2017/3678487 Text en Copyright © 2017 Wenjia Niu et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Niu, Wenjia
Xia, Kewen
Zu, Baokai
Bai, Jianchuan
Efficient Multiple Kernel Learning Algorithms Using Low-Rank Representation
title Efficient Multiple Kernel Learning Algorithms Using Low-Rank Representation
title_full Efficient Multiple Kernel Learning Algorithms Using Low-Rank Representation
title_fullStr Efficient Multiple Kernel Learning Algorithms Using Low-Rank Representation
title_full_unstemmed Efficient Multiple Kernel Learning Algorithms Using Low-Rank Representation
title_short Efficient Multiple Kernel Learning Algorithms Using Low-Rank Representation
title_sort efficient multiple kernel learning algorithms using low-rank representation
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5585640/
https://www.ncbi.nlm.nih.gov/pubmed/28912801
http://dx.doi.org/10.1155/2017/3678487
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