Discriminative sparsity preserving projections for image recognition

被引:62
作者
Gao, Quanxue [1 ]
Huang, Yunfang [1 ]
Zhang, Hailin [1 ]
Hong, Xin [1 ]
Li, Kui [1 ]
Wang, Yong [2 ]
机构
[1] Xidian Univ, State Key Lab Integrated Serv Networks, Xian 710071, Peoples R China
[2] Xidian Univ, Sch Elect Engn, Xian 710071, Peoples R China
基金
中国博士后科学基金; 中国国家自然科学基金;
关键词
Dimensionality reduction; Manifold learning; Sparse representation; Image Recognition; DIMENSIONALITY REDUCTION; FACE RECOGNITION; FRAMEWORK; GRAPH; REPRESENTATION; EIGENMAPS;
D O I
10.1016/j.patcog.2015.02.015
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Previous works have demonstrated that image classification performance can be significantly improved by manifold learning. However, performance of manifold learning heavily depends on the manual selection of parameters, resulting in bad adaptability in real-world applications. In this paper, we propose a new dimensionality reduction method called discriminative sparsity preserving projections (DSPP). Different from the existing sparse subspace algorithms, which manually construct a penalty adjacency graph, DSPP employs sparse representation model to adaptively build both intrinsic adjacency graph and penalty graph with weight matrix, and then integrates global within-class structure into the discriminant manifold learning objective function for dimensionality reduction. Extensive experimental results on four image databases demonstrate the effectiveness of the proposed approach. (C) 2015 Elsevier Ltd. All rights reserved.
引用
收藏
页码:2543 / 2553
页数:11
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