Supervised Group Sparse Representation via Intra-class Low-Rank Constraint
Group sparse representation (GSR) which uses the group structure of training samples as the prior knowledge has achieved extensive attention. However, GSR represents a new test sample using the original input features for least reconstruction, which may not be able to obtain discriminative reconstruction coefficients since redundant or noisy features may exist in the original input features. To obtain more discriminative data representation, in this paper we propose a novel supervised group sparse representation via intra-class low-rank constraint (GSRILC). Instead of representing the target by the original input features, GSRILC attempts to use the compact projection features in a new subspace for data reconstruction. Concretely, GSRILC projects data sharing the same class to a new subspace, and imposes low-rank constraint on the intra-class projections, which ensures that samples within the same class have a low rank structure. In this way, small intra-class distances and large inter-class distances can be achieved. To illustrate the effectiveness of the proposal, we conduct experiments on the Extended Yale B and CMU PIE databases, and results show the superiority of GSRILC.
KeywordsGroup sparse representation Intra-class low-rank Discriminative data representation Subspace learning
This work is supported in part by the Natural Science Foundation of China under Grants 61772141, 61702110, 61603100, Guangdong Provincial Natural Science Foundation under Grant 17ZK0422, Guangdong Provincial Science and Technology Project under Grants 2015B090901016, 2016B010108007, and Guangzhou Science and Technology Project under Grants 201804010347.
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