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New classifier based on compressed dictionary and LS-SVM
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DOI：10.1016/j.neucom.2016.08.024.png)
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摘要
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Inspired by the compressive sensing (CS) theory, a new classifier based on compressed dictionary and Least Squares Support Vector Machine (LS-SVM) is proposed to deal with large scale problems. The coefficients of support vectors can be recovered from a few measurements if LS-SVM is approximated to sparse structure. Using the known Choleslcy decomposition, we approximate the given kernel matrix to represent the coefficients of support vectors sparsely by a low-rank matrix that we have used as a dictionary. The proposed measurement matrix being coupled with the dictionary forms a compressed dictionary that proves to satisfy the restricted isometry property (RIP). Our classifier has the quality of low storage and computational complexity, high degree of sparsity and information preservation. Experiments on benchmark data sets show that our classifier has positive performance. (C) 2016 Elsevier B.V. All rights reserved.
Keyword:
Sparsity
Compressed dictionary
LS-SVM
Restricted isometry property
Compressive sensing
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期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
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