关键词: 核主成分分析（KPCA）, 稀疏KPCA, 核K-均值聚类, 说话人识别

Abstract: Aimming at the shortage of Sparse Kernel Principal Component Analysis（SKPCA） in feature extraction，a novel feature extraction method based on the kernel K-means clustering and the SKPCA for speaker recognition is proposed.Here kernel K-means clustering is to divide all the frames of each sample into a given amount of clusters，since the resulted clustering centers can represent better the clusters they belong to，the clustering is replaced by the clustering center，and the dimensions of kernel matrix are decreased accordingly.This method reduces storage and computational complexity，it guarantees to reduce data can represent the original data well and information loss is minimum.The experimental results show the proposed approach do not affect veracity，improve recognition rate，and meet the requirement of speaker recognition in terms of practicability.

Key words: Kernel Principal Component Analysis（KPCA）, sparse KPCA, kernel K-means clustering, speaker recognition