摘要
提出了一种残差加权的多元素协同表示算法.该算法针对SRC的单一鉴别性不足,对样本提出样本与字典的多元素分解并分别进行相应的协同表示,自适应地学习出多元素的残差权重并进行线性加权,从而提高分类的性能.实验表明:自适应残差加权的多元素协同表示分类算法,能够有效提高识别性能.
An adaptive weighted residuals multi-element collaborative representation classification is proposed in this paper. To address the weak discriminative power of SRC （sparse representation classifier） method, we propose using multiple elements to represent each element and construct multiple collaborative representation for classification. To reflect the different element with different importance and discriminative power, we present adaptive weighted residuals method to linearly combine different element representations for classification. Experimental results demonstrate the effectiveness and better classification accuracy of our proposed method.
出处
《计算机系统应用》
2014年第5期152-157,共6页
Computer Systems & Applications
基金
西安市科技计划项目(CX12179(1))
陕西省科技厅工业攻关项目(2011K06-13)
陕西省教育厅自然科学研究项目(11JK0985)
关键词
自适应残差权重
协同表示
分类算法
adaptive weighted residuals
Collaborative representation
SRC
