摘要
本文提出了改进的SKPCA降维方法。在特征向量稀疏化表达的基础上,实现了一阶降维、二阶降维与非线性降维。在增强特征向量表达能力的同时最大限度去除了冗余信息。实验证明利用改进的SKPCA降维方法获得的特征向量,检索效果优于SPCA方法。
The improved SKPCA method for dimensionality reduction is proposed in this paper. This method achieves an order,second -order and nonlinear dimensionality reduction. It can remove redundant information to the maximum extent. Experiments show that retrieval resuhs based on the improved SKPCA is better than SPCA.
出处
《智能计算机与应用》
2014年第5期29-31,共3页
Intelligent Computer and Applications
关键词
核主成份分析
降维
稀疏
特征向量
KPCA
Dimensionality Reduction
Sparse
Feature Vector
