摘要
在处理高维数据过程中,特征选择是一个非常重要的数据降维步骤。低秩表示模型具有揭示数据全局结构信息的能力和一定的鉴别能力。稀疏表示模型能够利用较少的连接关系揭示数据的本质结构信息。在低秩表示模型的基础上引入稀疏约束项,构建一种低秩稀疏表示模型学习数据间的低秩稀疏相似度矩阵;基于该矩阵提出一种低秩稀疏评分机制用于非监督特征选择。在不同数据库上将选择后的特征进行聚类和分类实验,同传统特征选择算法进行比较。实验结果表明了低秩特征选择算法的有效性。
Feature selection is an important reduce dimensional step in dealing with high dimensional data. Low-rank representation model shows a very good discriminative ability and can capture the global structure of the data. Sparse representation model can reveal the true intrinsic structure information with fewer connection relationships. Based on the low-rank representation model, sparse constraint items are added to construct a low-rank and sparse representation model, which is used to learn the low- rank and sparse affinity matrix between the data. Then with the obtained affinity matrix, we propose an unsupervised feature selection based on Low-Rank and Sparse Score （LRSS）. After the clustering and classification of the selected features on different databases we compare our proposal with traditional feature selection algorithms. Experimental results verify the effectiveness of our method, and show that our proposal outperforms the state-of-art feature selection approaches.
出处
《计算机工程与科学》
CSCD
北大核心
2015年第4期649-656,共8页
Computer Engineering & Science
基金
国家自然科学基金资助项目(51365017
61305019)
江西省科技厅青年科学基金资助项目(20132bab211032)
关键词
低秩表示
稀疏约束项
低秩稀疏评分
特征选择
low-rank representation
sparse constrains
low-rank and sparse score
feature select
作者简介
通信地址：341000江西省赣州市红旗大道86号江西理工大学电气工程与自动化学院,杨国亮（1973-），男，江西丰城人，博士，副教授，研究方向为智能控制、图像处理与模式识别。E-mail：ygliang30@126．com
