Multiple Laplacian graph regularised low‐rank representation with application to image representation. Issue 6 (21st April 2017)

• Record Type: Journal Article
• Authors: Shu, Zhenqiu; Fan, Hongfei; Huang, Pu; Wu, Dong; Ye, Feiyue; Wu, Xiaojun
• Is Part Of: IET image processing. Volume 11:Issue 6(2017)
• Page Start: 370
• Page End: 378
• Publication Date: 2017-04-21
• Subjects: image representation -- graph theory -- data structures -- search problems; multiple Laplacian graph regularised low‐rank representation -- LRR‐based techniques -- image representation -- data representation -- latent manifold structure -- cross validation -- discrete grid search -- high‐dimensional data -- multiple hypergraph regulariser -- augmented Lagrange multiplier -- real image datasets; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2016.0391
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Recently, low‐rank representation (LRR)‐based techniques have manifested remarkable results for data representation. To exploit the latent manifold structure of data, the graph regulariser is incorporated into the model of LRR. However, it is critical to construct an appropriate graph model and set the corresponding parameters. In addition, this procedure is usually time‐consuming and proved to be overfitting when using cross validation or discrete grid search. Two novel LRR‐based methods, called multiple graph regularised LRR and multiple hypergraph regularised LLR, are proposed to represent the high‐dimensional data. To guarantee the smoothness along the estimated manifold, the multiple graph regulariser and the multiple hypergraph regulariser are incorporated into the traditional LRR method, respectively, which results in a unified framework. Moreover, the augmented Lagrange multiplier is adopted to solve the proposed models. Extensive experiments on real image datasets show the effectiveness of the proposed methods.