Open AccessMultiple Laplacian graph regularised low‐rank representation with application to image representation
Author(s) -
Shu Zhenqiu,
Fan Hongfei,
Huang Pu,
Wu Dong,
Ye Feiyue,
Wu Xiaojun
Publication year - 2017
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2016.0391
Subject(s) - representation (politics) , graph , rank (graph theory) , laplacian matrix , mathematics , laplace operator , computer science , image (mathematics) , artificial intelligence , pattern recognition (psychology) , combinatorics , mathematical analysis , politics , political science , law
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.