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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.
Advanced Computer Control (ICACC), 2010 2nd International Conference on (Volume:4 )
Date of Conference: 27-29 March 2010