Robust Jointly Sparse Regression with Generalized Orthogonal Learning for Image Feature Selection. (September 2019)

• Record Type: Journal Article
• Authors: Mo, Dongmei; Lai, Zhihui
• Is Part Of: Pattern recognition. Volume 93(2019:Sep.)
• Page Start: 164
• Page End: 178
• Publication Date: 2019-09
• Subjects: Dimensionality reduction -- Local structure -- Joint sparsity -- Orthogonality -- Orthogonal matching pursuit; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2019.04.011
• Languages: English
• ISSNs: 0031-3203

Abstract

Abstract: Ridge regression (RR) and its variants are fundamental methods for multivariable data analysis, which have been widely used to deal with different problems in pattern recognition or classification. However, these methods have their common drawback. That is, the number of the learned projections is limited by the number of class. Moreover, most of these methods do not consider the local structure of the data, which makes them less competitive in the case when data are lying on a lower dimensional manifold. Therefore, in this paper, we propose a robust jointly sparse regression method to integrate the locality geometric structure with generalized orthogonality constraint and joint sparsity into a regression modal to address these problems. The optimization model can be solved by an alternatively iterative algorithm using orthogonal matching pursuit (OMP) and singular value decomposition. Experimental results on face and non-face image database demonstrate the superiority of the proposed method. The matlab code can be found at http://www.scholat.com/laizhihui .