Multi-state Information Dimension Reduction Based on Particle Swarm Optimization-Kernel Independent Component Analysis

• Published in: 东华大学学报（英文版） Vol. 34; no. 6; pp. 791 - 795
• Main Author: 邓士杰;苏续军;唐力伟;张英波
• Format: Journal Article
• Language: English
• Published: Department of Artillery Engineering, Ordnance Engineering College, Shijiazhuang 050003, China 01.12.2017
• Subjects: feature dimension reduction; particle swarm optimization (PSO); fitness function; kernel independent component analysis (KICA)
• ISSN: 1672-5220
• DOI: 10.3969/j.issn.1672-5220.2017.06.016

The precision of the kernel independent component analysis （KICA） algorithm depends on the type and parameter values of kernel function. Therefore, it＇s of great significance to study the choice method of KICA＇s kernel parameters for improving its feature dimension reduction result. In this paper, a fitness function was established by use of the ideal of Fisher discrimination function firstly. Then the global optimal solution of fitness function was searched by particle swarm optimization （PSO） algorithm and a multi-state information dimension reduction algorithm based on PSO-KICA was established. Finally, the validity of this algorithm to enhance the precision of feature dimension reduction has been proven.