自适应学习的多特征元素协同表示分类算法

doi:10.11772/j.issn.1001-9081.2014.04.1094

计算机应用 ›› 2014, Vol. 34 ›› Issue (4): 1094-1098.DOI: 10.11772/j.issn.1001-9081.2014.04.1094

自适应学习的多特征元素协同表示分类算法

王建仁¹,魏龙¹,段刚龙¹,黄梯云²

1. 西安理工大学经济与管理学院,西安 710054;
2. 哈尔滨工业大学管理学院,哈尔滨 150001

收稿日期:2013-09-27 修回日期:2013-12-16 出版日期:2014-04-01 发布日期:2014-04-29
通讯作者: 魏龙
作者简介:王建仁(1961-),男,山西运城人,副教授,硕士,主要研究方向:智能信息处理、数据挖掘;
魏龙(1988-),男,陕西西安人,硕士研究生,主要研究方向:数据挖掘;
段刚龙 (1977-),男,陕西大荔人,讲师,硕士,主要研究方向:数据挖掘;
黄梯云 (1932-),男,安徽休宁人,教授,博士生导师,主要研究方向:管理信息系统、智能决策支持系统、数据挖掘。
基金资助:
陕西省教育厅自然科学研究项目;陕西省科技厅工业攻关项目;西安市科技计划项目

Self-adaptive learning algorithm for collaborative representation classification of multi-feature elements

WANG Jianren¹,WEI Long¹,DUAN Ganglong¹,HUANG Tiyun²

1. Faculty of Economics and Management, Xi'an University of Technology, Xi'an Shaanxi 710054, China
2. School of Management, Harbin Institute of Technology, Harbin Heilongjiang 150001, China

Received:2013-09-27 Revised:2013-12-16 Online:2014-04-01 Published:2014-04-29
Contact: WEI Long

摘要/Abstract

摘要：

针对基于传统的稀疏表示分类算法的单特征鉴别性较弱这一不足,提出一种基于自适应学习的多特征元素协同表示分类算法SLMCE_CRC。该算法结合多特征子字典的思想,对样本提出特征元素的双重分解,并分别从特征和元素角度分别进行相应的协同表示,自适应地学习出各个特征的稀疏权重和元素的残差权重,并进行线性加权,从而实现目标的分类。实验结果表明,使用该方法能显著提高识别率,尤其对含有较多特征细节的图像数据,具有一定的实用价值。

Abstract:

To address the weak discriminative power of Sparse Representation Classification (SRC), a self-adaptive learning algorithm for collaborative representation classification of multi-feature elements named SLMCE_CRC was proposed. Based on the idea of multi-feature sub-dictionary, the sample was collaboratively represented by features and elements, the sparse weights of features and the residual weights of elements were learnd self-adaptively and combined linearly to classify the samples. The experimental results demonstrate the effectiveness and high classification accuracy of the proposed algorithm. It is suitable to images with multi-features.

中图分类号:

TP391.41

王建仁魏龙段刚龙黄梯云. 自适应学习的多特征元素协同表示分类算法[J]. 计算机应用, 2014, 34(4): 1094-1098.

WANG Jianren WEI Long DUAN Ganglong HUANG Tiyun. Self-adaptive learning algorithm for collaborative representation classification of multi-feature elements[J]. Journal of Computer Applications, 2014, 34(4): 1094-1098.

参考文献

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自适应学习的多特征元素协同表示分类算法

Self-adaptive learning algorithm for collaborative representation classification of multi-feature elements

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