An Improvement Algorithm of Principal Component Analysis

摘要：

The conventional method of principal component analysis (PCA) is reducing data dimensions directly from m to k (k<m) by one step. The lost information of PCA is holistically determined by the k. To reduce the lost information in the case of k is determined, we decrease the dimensions of the data from m to k by n(1 ≤n ≤(m-k))steps. This new PCA method is called multi-step PCA (MPCA). The algorithm of MPCA is shown in the article. Two linear Neural Networks based on the PCA or MPCA is analyzed. Compared the PCA with MPCA and compared the numeric algorithm with Neural Networks, we find that the correct classification capability of MPCA is some better than the PCA and the correct classification capability o f Neural Networks is some better than the numeric algorithm.

关键词： Principal Component Analysis (PCA) Multi-step PCA Neural Networks Pattern Recognition

作者: Yu Chuanqiang Guo Xiaosong Zhang An Pan Xingjie

作者单位: Xian Research Inst.Of Hi-Tech Hongqing Town,Xian 710025,China

会议类型: 国际会议

会议名称: 第八届国际电子测量与仪器学术会议(Proceedings of 2007 8th International Conference on Electronic Measurement & Instruments)

会议地点: 西安

会议语种:英文

在线出版日期: 2007-08-16（万方平台首次上网日期，不代表论文的发表时间）