Random subspace based ensemble sparse representation. (February 2018)

• Record Type: Journal Article
• Authors: Gu, Jing; Jiao, Licheng; Liu, Fang; Yang, Shuyuan; Wang, Rongfang; Chen, Puhua; Cui, Yuanhao; Xie, Junhu; Zhang, Yake
• Is Part Of: Pattern recognition. Volume 74(2018:Feb.)
• Page Start: 544
• Page End: 555
• Publication Date: 2018-02
• Subjects: Random subspace -- Sparse representation -- Clustering -- Semi-supervised classification; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2017.09.016
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: A new random subspace based joint sparse representation model is proposed. The sparse representations in multiple subspaces are integrated into an ensemble sparse representation. The spectral clustering algorithm with random subspace based ensemble sparse representation is shown. The semi-supervised classification method with random subspace based ensemble sparse representation is described. Abstract: In this paper, a new random subspace based ensemble sparse representation (RS_ESR) algorithm is proposed, where the random subspace is introduced into sparse representation model. For high-dimensional data, the random subspace method can not only reduce dimension of data but also make full use of effective information of data. It is not like traditional dimensionality reduction methods that may lose some information of original data. Additionally, a joint sparse representation model is emloyed to obtain the sparse representation of a sample set in the low dimensional random subspace. Then the sparse representations in multiple random subspaces are integrated as an ensemble sparse representation. Moreover, the obtained RS_ESR is applied in classical clustering and semi-supervised classification. The experimental results on different real-world data sets show the superiority of RS_ESR over traditional methods.