点击次数：

论文类型：期刊论文

发表时间：2011-07-01

发表刊物：EXPERT SYSTEMS WITH APPLICATIONS

收录刊物：Scopus、SCIE、EI

卷号：38

期号：7

页面范围：8138-8143

ISSN号：0957-4174

关键字：Support vector clustering; Minimal enclosing sphere; Maximum entropy; Adjacency matrix; Kernel matrix

摘要：The support vector clustering (SVC) algorithm consists of two main phases: SVC training and cluster assignment. The former requires calculating Lagrange multipliers and the latter requires calculating adjacency matrix, which may cause a high computational burden for cluster analysis. To overcome these difficulties, in this paper, we present an improved SVC algorithm. In SVC training phase, an entropy-based algorithm for the problem of calculating Lagrange multipliers is proposed by means of Lagrangian duality and the Jaynes' maximum entropy principle, which evidently reduces the time of calculating Lagrange multipliers. In cluster assignment phase, the kernel matrix is used to preliminarily classify the data points before calculating adjacency matrix, which effectively reduces the computing scale of adjacency matrix. As a result, a lot of computational savings can be achieved in the improved algorithm by exploiting the special structure in SVC problem. Validity and performance of the proposed algorithm are demonstrated by numerical experiments. Crown Copyright (C) 2010 Published by Elsevier Ltd. All rights reserved.