DocumentCode
3601094
Title
Twin Support Vector Machine for Clustering
Author
Zhen Wang ; Yuan-Hai Shao ; Lan Bai ; Nai-Yang Deng
Author_Institution
Sch. of Math. Sci., Inner Mongolia Univ., Hohhot, China
Volume
26
Issue
10
fYear
2015
Firstpage
2583
Lastpage
2588
Abstract
The twin support vector machine (TWSVM) is one of the powerful classification methods. In this brief, a TWSVM-type clustering method, called twin support vector clustering (TWSVC), is proposed. Our TWSVC includes both linear and nonlinear versions. It determines k cluster center planes by solving a series of quadratic programming problems. To make TWSVC more efficient and stable, an initialization algorithm based on the nearest neighbor graph is also suggested. The experimental results on several benchmark data sets have shown a comparable performance of our TWSVC.
Keywords
graph theory; pattern classification; pattern clustering; quadratic programming; support vector machines; TWSVC; TWSVM-type clustering method; benchmark data sets; classification methods; initialization algorithm; k-cluster center planes; linear system; nearest neighbor graph; nonlinear system; quadratic programming problems; twin-support vector clustering; twin-support vector machine; Accuracy; Benchmark testing; Clustering algorithms; Clustering methods; Learning systems; Manifolds; Support vector machines; Manifold clustering; plane-based clustering; twin support vector machine (TWSVM); unsupervised learning; unsupervised learning.;
fLanguage
English
Journal_Title
Neural Networks and Learning Systems, IEEE Transactions on
Publisher
ieee
ISSN
2162-237X
Type
jour
DOI
10.1109/TNNLS.2014.2379930
Filename
7001711
Link To Document