基于局部保持的核稀疏表示字典学习

陈思宝; 赵令; 罗斌

doi:10.3724/SP.J.1004.2014.02295

基于局部保持的核稀疏表示字典学习

doi: 10.3724/SP.J.1004.2014.02295

陈思宝^1,2,
赵令^1,2,
罗斌^1,2

1.
安徽大学计算机科学与技术学院合肥 230601;
2.
安徽省工业图像处理与分析重点实验室合肥 230039

基金项目:

国家自然科学基金 (61202228, 610731116),高等学校博士学科点专项科研基金(20103401120005),安徽省高校自然科学研究重点项目(KJ2012A004, KJ2012A008)资助

详细信息

作者简介:
赵令安徽大学计算机科学与技术学院硕士研究生.主要研究方向为图像处理与模式识别.E-mail: ahuzl1990@hotmail.com

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- 文章访问数: 1963
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- 被引次数: 0
出版历程
- 收稿日期: 2014-01-09
- 修回日期: 2014-04-10
- 刊出日期: 2014-10-20

Locality Preserving Based Kernel Dictionary Learning for Sparse Representation

CHEN Si-Bao^1,2,
ZHAO Ling^1,2,
LUO Bin^1,2

1.
School of Computer Science and Technology, Anhui University, Hefei 230601;
2.
Key Laboratory for Industrial Image Processing and Analysis of Anhui Province, Hefei 230039

Funds:

Supported by National Natural Science Foundation of China (61202228,610731116), Doctoral Program Foundation of Institutions of Higher Education of China (20103401120005), and Collegiate Natural Science Fund of Anhui Province (KJ2012A004, KJ2012A008)

摘要

摘要: 为了利用核技巧提高分类性能, 在局部保持的稀疏表示字典学习的基础上, 提出了两种核化的稀疏表示字典学习方法. 首先, 原始训练数据被投影到高维核空间, 进行基于局部保持的核稀疏表示字典学习; 其次, 在稀疏系数上强加核局部保持约束, 进行基于核局部保持的核稀疏表示字典学习. 实验结果表明, 该方法的分类识别结果优于其他方法.
- 字典学习 /
- 稀疏表示 /
- 核空间 /
- 局部保持
Abstract: In order to further improve the classification performance via kernel tricks, two new kernel dictionary learning methods are proposed for sparse representation, which are extended from dictionary learning via locality preserving for sparse representation (LPDL). First, the original training data are projected into a high dimensional kernel space, then locality preserving based kernel dictionary learning for sparse representation (LPKDL) is proposed. Second, the kernelized locality preserving criterion is imposed on the sparse coefficients, and then the kernelized locality preserving based kernel dictionary learning for sparse representation (KLPKDL) is proposed. Experimental results show that the proposed methods are superior to other methods on classification performances.
- Dictionary learning /
- sparse representation /
- kernel space /
- locality preserving

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参考文献(22)

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