An improved algorithm for support vector clustering based on maximum entropy principle and kernel matrix |
| |
| Authors: | Chonghui Guo Fang Li |
| |
| Affiliation: | 1. School of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning 116023, China;2. School of Life Science and Biotechnology, Dalian University of Technology, Dalian, Liaoning 116023, China;1. College of Computer Science and Technology, Jilin University, Changchun, Jilin 130012, China;2. School of Computing, University of Eastern Finland, Joensuu FIN-80101, Finland;3. School of Electrical Engineering, Shanghai Dianji University, Shanghai 200240, China;4. School of Physics and Electronic Information, Wenzhou University, Wenzhou 325035, China;1. Department of Industrial Engineering and Management, Shanghai Jiao Tong University, 200240 Shanghai, China;2. Sino-US Global Logistics Institute, Shanghai Jiao Tong University, 200030 Shanghai, China;3. Mines Saint-Etienne, CNRS, UMR 6158 LIMOS, Centre CIS, F-42023 Saint-Etienne, France;4. Antai College of Economics and Management, Shanghai Jiao Tong University, 200030 Shanghai, China |
| |
| Abstract: | 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. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
