DocumentCode :
2509298
Title :
Semi-supervised Graph Learning: Near Strangers or Distant Relatives
Author :
Chen, Weifu ; Feng, Guocan
Author_Institution :
Sch. of Math. & Comput. Sci., Sun Yat-sen Univ., Guangzhou, China
fYear :
2010
fDate :
23-26 Aug. 2010
Firstpage :
3368
Lastpage :
3371
Abstract :
In this paper, an easily implemented semi-supervised graph learning method is presented for dimensionality reduction and clustering, using the most of prior knowledge from limited pairwise constraints. We extend instance-level constraints to space-level constraints to construct a more meaningful graph. By decomposing the (normalized) Laplacian matrix of this graph, to use the bottom eigenvectors leads to new representations of the data, which are hoped to capture the intrinsic structure. The proposed method improves the previous constrained learning methods. Furthermore, to achieve a given clustering accuracy, fewer constraints are required in our method. Experimental results demonstrate the advantages of the proposed method.
Keywords :
Laplace equations; data structures; graph theory; matrix algebra; pattern clustering; Laplacian matrix; constrained learning method; data representations; dimensionality clustering; dimensionality reduction; distant relatives; eigenvectors; instance-level constraints; near strangers; semi-supervised graph learning; space-level constraints; Accuracy; Clustering algorithms; Distortion measurement; Laplace equations; Learning systems; Moon; Symmetric matrices;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition (ICPR), 2010 20th International Conference on
Conference_Location :
Istanbul
ISSN :
1051-4651
Print_ISBN :
978-1-4244-7542-1
Type :
conf
DOI :
10.1109/ICPR.2010.822
Filename :
5597504
Link To Document :
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