Data Dimension Reduction Based on Kernel Entropy Component Analysis

Aiming at the curse of dimensionality,the kernel entropy component analysis(KECA) is used to reduce the dimension of data,which is compared with Principal Component Analysis(PCA) and Kernel PCA(KPCA).The low dimensional data after dimension reduction are classified by Support Vector Machine(SVM) algorithm to compare the accuracy.Experimental results indicate that high classification accuracy can be obtained at low dimension number with KECA,which reduces the processing complexity and running time.It suggests that KECA-based dimension reduction algorithm has the feasibility to be applied in the fields of machine learning,pattern recognition,etc.