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。
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
出处
《自动化学报》
EI
CSCD
北大核心
2014年第10期2295-2305,共11页
Acta Automatica Sinica
基金
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~~
关键词
字典学习
稀疏表示
核空间
局部保持
Dictionary learning, sparse representation, kernel space, locality preserving
