Graph Construction Based on Re-weighted Sparse Representation for Semi-supervised Learning
Yikai Wang, Yueqi Zhao, Longtong Zhang, Jiaming Liang, Maoxi Zeng, Xiaolan Liu
Graph Construction Based on Re-weighted Sparse Representation for Semi-supervised Learning
Yikai Wang, Yueqi Zhao, Longtong Zhang, Jiaming Liang, Maoxi Zeng, Xiaolan Liu
Abstract :Graph construction is the key part of graph-based semi-supervised
learning algorithms, and the performance of algorithms
depends heavily on the graph weight matrix given by graph
construction process. In this paper, we propose a modified L1 graph
construction model based on the distance of sample pairs. The
solutions of the proposed method include the distance of sample
pairs to improve the accuracy. Numerical experiments on face
datasets indicate that the results yielded by the proposed algorithm
perform better than the algorithms based on traditional graph
construction methods and L1 graph.
Key words :
Semi-supervised Learning
Graph Construction
Sparse Representation
Face Recognition
引用本文:
Yikai Wang;Yueqi Zhao;Longtong Zhang;Jiaming Liang;Maoxi Zeng;Xiaolan Liu. Graph Construction Based on Re-weighted Sparse Representation for Semi-supervised Learning[J]. , 2013, 10(2): 375-383.
Yikai Wang;Yueqi Zhao;Longtong Zhang;Jiaming Liang;Maoxi Zeng;Xiaolan Liu. Graph Construction Based on Re-weighted Sparse Representation for Semi-supervised Learning. , 2013, 10(2): 375-383.
链接本文:
http://manu35.magtech.com.cn/Jwk_ics/CN/ 或 http://manu35.magtech.com.cn/Jwk_ics/CN/Y2013/V10/I2/375
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