Incremental Principal Component Analysis Based on Reduced Subspace Projection

摘要：

Subspace projection (SP) is a kind of efficient subspace tracking algorithm, and it is an incremental principal component analysis algorithm too. In this paper the SP algorithm is first analyzed in detail; then, based on the eigenvectors property the computation complexity of SP is reduced from 0(N (P + l)) to O(N ); finally, the covariance matrix is replaced with approximated covariance matrix which is composed of large eigenvalues and their corresponding eigenvectors, the computation complexity can be reduced to O(N(P + 1)) further. Experiment results based on ORL face database demonstrate the efficiency of our proposed algorithm.

关键词： subspace projection incremental principal component eigenvalue approximated covariance matrix

作者: Cao Xiang-hai

作者单位: School of Electronic Engineering Xidian University Xian 710071 China

会议类型: 国际会议

会议名称: The 2nd IEEE International Conference on Advanced Computer Control(第二届先进计算机控制国际会议 ICACC 2010)

会议地点: 沈阳

会议语种:英文

页码: 602-605

在线出版日期: 2010-03-27（万方平台首次上网日期，不代表论文的发表时间）