Dual Pursuit for Subspace Learning

被引:19
|
作者
Yi, Shuangyan [1 ]
Liang, Yingyi [1 ]
He, Zhenyu [1 ]
Li, Yi [1 ]
Cheung, Yiu-Ming [2 ,3 ,4 ]
机构
[1] Harbin Inst Technol, Sch Comp Sci & Technol, Shenzhen 518055, Peoples R China
[2] Hong Kong Baptist Univ, Dept Comp Sci, Hong Kong, Peoples R China
[3] Hong Kong Baptist Univ, Inst Res & Continuing Educ, Hong Kong, Peoples R China
[4] Beijing Normal Univ HKBU, United Int Coll, Zhuhai 519000, Peoples R China
基金
中国国家自然科学基金;
关键词
Low-rank representation; dual pursuit; graph-regularization technique; NONNEGATIVE LOW-RANK; MOTION SEGMENTATION; SALIENCY DETECTION; SPARSE GRAPH; REPRESENTATION;
D O I
10.1109/TMM.2018.2877888
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In general, low-rank representation (LRR) aims to find the lowest rank representation with respect to a dictionary. In fact, the dictionary is a key aspect of low-rank representation. However, a lot of low-rank representation methods usually use the data itself as a dictionary (i.e., a fixed dictionary), which may degrade their performances due to the lack of clustering ability of a fixed dictionary. To this end, we propose learning a locality-preserving dictionary instead of the fixed dictionary for low-rank representation, where the locality-preserving dictionary is constructed by using a graph regularization technique to capture the intrinsic geometric structure of the dictionary and, hence, the locality-preserving dictionary has an underlying clustering ability. In this way, the obtained low-rank representation via the locality-preserving dictionary has a better grouping-effect representation. Inversely, a better grouping-effect representation can help to learn a good dictionary. The locality-preserving dictionary and the grouping-effect representation interact with each other, where dual pursuit is called. The proposed method, namely, Dual Pursuit for Subspace Learning, provides us with a robust method for clustering and classification simultaneously, and compares favorably with the other state-of-the-art methods.
引用
收藏
页码:1399 / 1411
页数:13
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