Abstract
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 eigenvector's property the computation complexity of SP is reduced from O(N2(P+1)) to O(N2); 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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
