Multiple Kernel Sparse Representation-Based Classification

CHEN Si-bao; XU Li-xian; LUO Bin

doi:10.3969/j.issn.0372-2112.2014.09.022

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Multiple Kernel Sparse Representation-Based Classification

更新时间：2025-07-16

- Multiple Kernel Sparse Representation-Based Classification
- Acta Electronica Sinica Vol. 42, Issue 9, Pages: 1807-1811(2014)
- 作者机构：
  
  1. 安徽大学计算机科学与技术学院,安徽,合肥,230601
  2. 安徽省工业图像处理与分析重点实验室,安徽,合肥,230039
  3. 安徽大学计算机科学与技术学院,安徽,合肥,230601
  4. 安徽省工业图像处理与分析重点实验室,安徽,合肥,230039
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61202228, No.61073116);Research Fund for Doctoral Programs in Higher Education Institutions (No.20103401120005);Key Program of Natural Science Research Projrct of Colleges and Universities of Anhui Province (No.KJ2012A004)
- DOI：10.3969/j.issn.0372-2112.2014.09.022
  CLC： TP391.4
- Published：2014
- 稿件说明：
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CHEN Si-bao, XU Li-xian, LUO Bin. Multiple Kernel Sparse Representation-Based Classification[J]. Acta Electronica Sinica, 2014, 42(9): 1807-1811.
DOI：

CHEN Si-bao, XU Li-xian, LUO Bin. Multiple Kernel Sparse Representation-Based Classification[J]. Acta Electronica Sinica, 2014, 42(9): 1807-1811. DOI： 10.3969/j.issn.0372-2112.2014.09.022.

摘要

稀疏表示分类（SRC）及核方法在模式识别的很多问题中都得到了成功的运用.为了提高其分类精度，提出多重核稀疏表示及其分类（MKSRC）方法.提出一种快速求解稀疏系数的优化迭代方法并给出了其收敛到全局最优解的证明.对于多重核的权重给出了两种自动更新方式并进行了分析与比较.在不同的人脸图像库上的分类实验显示了所提出的多重核稀疏表示分类的优越性.

Abstract

Sparse representation based classification (SRC) and kernel methods are applied in many pattern recognition problems.In order to improve the classification accuracy

we propose multiple kernel sparse representation based classification (MKSRC).A fast optimization iteration method to solve sparse coefficients and the associated convergence proof to global optimal solution are given.In order to update the kernel weights of MKSRC

two different updating methods and the associated comparison are given.The experimental results on three face image databases show the superiority of the proposed multiple kernel sparse representation based classification.

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