Open AccessUsing composite low rank and sparse graph for label propagation
Author(s) -
Guo Junjun,
Wu Daiwen
Publication year - 2014
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2013.2391
Subject(s) - composite number , graph , rank (graph theory) , computer science , mathematics , algorithm , pattern recognition (psychology) , combinatorics , artificial intelligence
Based on the low rank representation (LRR) and the sparse representation (SR), a composite LRR with SR graph LRRSR for semi‐supervised label propagation is proposed. The LRRSR aims to capture both the global structure of the data by a low rank constraint and the local structure of the data by a sparse constraint simultaneously. A composite framework is applied to fuse the two graphs. Then, a label propagation framework is used to transmit the labels from the labelled samples to the unlabelled samples. It is applied on several face image datasets and the experimental results demonstrate its good performance for face classification with a limited number of labelled samples.