Open AccessSemi‐supervised low‐rank representation graph for pattern recognition
Author(s) -
Yang Shuyuan,
Wang Xiuxiu,
Wang Min,
Han Yue,
Jiao Licheng
Publication year - 2013
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.2012.0322
Subject(s) - pattern recognition (psychology) , computer science , graph , artificial intelligence , representation (politics) , rank (graph theory) , mathematics , theoretical computer science , combinatorics , politics , political science , law
In this study, the authors propose a new semi‐supervised low‐rank representation graph for pattern recognition. A collection of samples is jointly coded by the recently developed low‐rank representation (LRR), which better captures the global structure of data and implements more robust subspace segmentation from corrupted samples. By using the calculated LRR coefficients of both labelled and unlabelled samples as the graph weights, a low‐rank representation graph is established in a parameter‐free manner under the framework of semi‐supervised learning. Some experiments are taken on the benchmark database to investigate the performance of the proposed method and the results show that it is superior to other related semi‐supervised graphs.