电子学报2018，Vol.46Issue(4)：930-937,8.DOI:10.3969/j.issn.0372-2112.2018.04.022
基于局部空间变稀疏约束的多核学习方法
Local Variable Sparsity Based Multiple Kernel Learning Algorithm
摘要
Abstract
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.关键词
多核学习/支持向量机/局部学习/变稀疏约束Key words
multiple kernel learning/support vector machine/local learning/variable sparsity constraint分类
信息技术与安全科学引用本文复制引用
王庆超,付光远,汪洪桥,辜弘扬,王超..基于局部空间变稀疏约束的多核学习方法[J].电子学报,2018,46(4):930-937,8.基金项目
国家自然科学基金(No.61202332,No.61403397) （No.61202332,No.61403397）
陕西省自然科学基金(No.2015JM6313) （No.2015JM6313）
