DocumentCode :
2916003
Title :
Nonnegative sparse coding for discriminative semi-supervised learning
Author :
He, Ran ; Zheng, Wei-Shi ; Hu, Bao-Gang ; Kong, Xiang-Wei
Author_Institution :
Inst. of Autom., Chinese Acad. of Sci., Beijing, China
fYear :
2011
fDate :
20-25 June 2011
Firstpage :
2849
Lastpage :
2856
Abstract :
An informative and discriminative graph plays an important role in the graph-based semi-supervised learning methods. This paper introduces a nonnegative sparse algorithm and its approximated algorithm based on the l0-l1 equivalence theory to compute the nonnegative sparse weights of a graph. Hence, the sparse probability graph (SPG) is termed for representing the proposed method. The nonnegative sparse weights in the graph naturally serve as clustering indicators, benefiting for semi-supervised learning. More important, our approximation algorithm speeds up the computation of the nonnegative sparse coding, which is still a bottle-neck for any previous attempts of sparse non-negative graph learning. And it is much more efficient than using l1-norm sparsity technique for learning large scale sparse graph. Finally, for discriminative semi-supervised learning, an adaptive label propagation algorithm is also proposed to iteratively predict the labels of data on the SPG. Promising experimental results show that the nonnegative sparse coding is efficient and effective for discriminative semi-supervised learning.
Keywords :
approximation theory; graph theory; iterative decoding; learning (artificial intelligence); pattern clustering; probability; sparse matrices; SPG; adaptive label propagation algorithm; approximation algorithm; clustering indicators; discriminative graph; discriminative semisupervised learning; graph-based semisupervised learning method; informative graph; l0-l1 equivalence theory; l1-norm sparsity technique; large scale sparse graph learning; nonnegative sparse coding; nonnegative sparse weights algorithm; sparse nonnegative graph learning; sparse probability graph; Clustering algorithms; Databases; Encoding; Machine learning; Prediction algorithms; Sparse matrices; Training;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on
Conference_Location :
Providence, RI
ISSN :
1063-6919
Print_ISBN :
978-1-4577-0394-2
Type :
conf
DOI :
10.1109/CVPR.2011.5995487
Filename :
5995487
Link To Document :
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