Local Variable Sparsity Based Multiple Kernel Learning Algorithm

被引：0

作者：

Fu G.-Y. ^{[1
]}

Wang Q.-C. ^{[1
]}

Wang H.-Q. ^{[1
]}

Gu H.-Y. ^{[1
]}

Wang C. ^{[1
]}

机构：

[1] The Rocket Force University of Engineering, Xi'an, 710025, Shaanxi

来源：

| 2018年 / Chinese Institute of Electronics卷 / 46期

关键词：

Local learning; Multiple kernel learning; Support vector machine; Variable sparsity constraint;

D O I：

10.3969/j.issn.0372-2112.2018.04.022

中图分类号：

学科分类号：

摘要：

Local multiple kernel learning method could learn a specific combination kernel function for various samples according to the local space characteristics, therefore it has better discriminant ability. In this paper, we propose a local variable sparsity based multiple kernel learning method. In our method, the samples are divided into a few groups with a soft grouping method and the sparsity of kernel weights in various local spaces is determined by the similarity of kernels. We use an alternative optimization method to solve this problem. The experiment on synthetic dataset indicates that our method has a strong advantage in discriminative feature learning and against noise. Finally we apply our method into image scene classification and the accuracy is improved obviously. © 2018, Chinese Institute of Electronics. All right reserved.

引用

页码：930 / 937

页数：7

共 32 条

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