Multi-state Information Dimension Reduction Based on Particle Swarm Optimization-Kernel Independent Component Analysis - 邓士杰;苏续军;唐力伟;张英波 (东华大学学报（英文版）, 2017)
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.