Abstract:

For the problems of a large amount of calculations, unremovable redundant information and unconsidered correlation in the traditional dimensionality reduction method for the image feature, a method based on the fast principal component analysis (FPCA) algorithm and the ReliefF algorithm was proposed. Firstly, the FPCA algorithm was used for the initial dimensionality reduction of the sample data to remove the redundant information; then the ReliefF algorithm was used to calculate the classification weights of the sample features which were used to perform optimized combination of features. In the algorithm implementation process, the recursive remove strategy was used to further enhance the ability of the algorithm to find the optimal characteristics. Simulation results show that the image features selected by the algorithm in this paper can better improve the clustering result, which is very suitable for practical engineering application.

Key words: image feature, dimensionality reduction, fast principal component analysis(FPCA), ReliefF algorithm