Training Data Reduction and Classification Based on Greedy Kernel Principal Component Analysis and Fuzzy C-means Algorithm
Nonlinear feature extraction used standard Kernel Principal Component Analysis (KPCA) method has large memories and high computational complexity in large datasets.A Greedy Kernel Principal Component Analysis (GKPCA) method is applied to reduce training data and deal with the nonlinear feature extraction problem for training data of large data in classification.First,a subset,which approximates to the original training data,is selected from the full training data using the greedy technique of the GKPCA method.Then,the feature extraction model is trained by the subset instead of the full training data.Finally,FCM algorithm classifies feature extraction data of the GKPCA,KPCA and PCA methods,respectively.The simulation results indicate that the feature extraction performance of both the GKPCA,and KPCA methods outperform the PCA method.In addition of retaining the performance of the KPCA method,the GKPCA method reduces computational complexity due to the reduced training set in classification.
training data reduction classification nonlinear feature extraction greedy kernel principal component analysis fuzzy C-means algorithm kernel matrix
Xiaofang Liu Chun Yang
School of Computer Science Sichuan University of Science and Engineering Zigong,China School of Economics and Management Sichuan University of Science and Engineering Zigong,China
国际会议
杭州
英文
2394-2397
2013-03-22（万方平台首次上网日期，不代表论文的发表时间）