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
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.;
Journal_Title :
Neural Networks and Learning Systems, IEEE Transactions on
DOI :
10.1109/TNNLS.2014.2379930
