DocumentCode :
671687
Title :
Non-parallel semi-supervised classification based on kernel spectral clustering
Author :
Mehrkanoon, Siamak ; Suykens, Johan A. K.
Author_Institution :
Dept. of Electr. Eng., Katholieke Univ. Leuven, Leuven, Belgium
fYear :
2013
fDate :
4-9 Aug. 2013
Firstpage :
1
Lastpage :
8
Abstract :
In this paper, a non-parallel semi-supervised algorithm based on kernel spectral clustering is formulated. The prior knowledge about the labels is incorporated into the kernel spectral clustering formulation via adding regularization terms. In contrast with the existing multi-plane classifiers such as Multisurface Proximal Support Vector Machine (GEPSVM) and Twin Support Vector Machines (TWSVM) and its least squares version (LSTSVM) we will not use a kernel-generated surface. Instead we apply the kernel trick in the dual. Therefore as opposed to conventional non-parallel classifiers one does not need to formulate two different primal problems for the linear and nonlinear case separately. The proposed method will generate two non-parallel hyperplanes which then are used for out-of-sample extension. Experimental results demonstrate the efficiency of the proposed method over existing methods.
Keywords :
learning (artificial intelligence); pattern classification; pattern clustering; dual formulation; kernel spectral clustering; kernel trick; nonparallel hyperplane generation; nonparallel semisupervised classification algorithm; out-of-sample extension; regularization terms; Clustering algorithms; Kernel; Linear programming; Optimization; Support vector machines; Training data; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks (IJCNN), The 2013 International Joint Conference on
Conference_Location :
Dallas, TX
ISSN :
2161-4393
Print_ISBN :
978-1-4673-6128-6
Type :
conf
DOI :
10.1109/IJCNN.2013.6707029
Filename :
6707029
Link To Document :
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