Local Variable Sparsity Based Multiple Kernel Learning Algorithm

WANG Qing-chao; FU Guang-yuan; WANG Hong-qiao; GU Hong-yang; WANG Chao

doi:10.3969/j.issn.0372-2112.2018.04.022

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Local Variable Sparsity Based Multiple Kernel Learning Algorithm

更新时间：2025-07-16

- Local Variable Sparsity Based Multiple Kernel Learning Algorithm
- Acta Electronica Sinica Vol. 46, Issue 4, Pages: 930-937(2018)
- 作者机构：
  
  火箭军工程大学信息工程系,陕西,西安,710025
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2018.04.022
  CLC： TP181
- Published Online：25 April 2018，
  
  Published：2018
- 稿件说明：
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WANG Qing-chao, FU Guang-yuan, WANG Hong-qiao, et al. Local Variable Sparsity Based Multiple Kernel Learning Algorithm[J]. Acta Electronica Sinica, 2018, 46(4): 930-937.
DOI：

WANG Qing-chao, FU Guang-yuan, WANG Hong-qiao, et al. Local Variable Sparsity Based Multiple Kernel Learning Algorithm[J]. Acta Electronica Sinica, 2018, 46(4): 930-937. DOI： 10.3969/j.issn.0372-2112.2018.04.022.

摘要

局部多核学习方法根据样本所在局部空间特性选择特定的核函数组合方式，具有较好的判别能力.本文提出了一种基于局部空间变稀疏约束的多核学习方法，首先依据样本在特征空间的分布情况以软分组的方式将训练数据划分为若干数据子集.以数据子集为单位，根据在相应的局部空间内的核函数相似程度，调整核组合的稀疏程度，使用交替优化的方法进行求解.实验表明本文方法对于区分特征学习和对抗噪声方面具有的优势，因此也使得在图像场景分类问题上的准确率和稳定性得到明显提高.

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.

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