[1]Zhao H T, Yuen P C, Kwok J T. A Novel Incremental Principal Component Analysis and Its Application for Face Recognition[J]. IEEE Trans on CMC, 2006, 36(4): 873-886.
[2]Hall P, Marshall D, Martin R. Merging and Splitting Eigenspace Models[J]. IEEE Trans on PA and MI, 2000, 22(9): 1042-1049.
[3]Weng J Y, Zhang Y L, Hwang W S. Candid Covariance-free Incremental Principal Component Analysis[J]. IEEE Trans on PA and MI, 2003, 25 (8):1034-1040.
[4]Ragothaman P,Yang T, Mikhael W B, et al. Efficient Adaptive Subspace Tracking Algorithm for Automatic Target Recognition[J]. Electronics Letters, 2006, 42(20): 1183-1184.
[5]Davila C E. Efficient, High Performance, Subspace Tracking for Time-domain Data[J]. IEEE Trans on SP, 2000, 48(12): 3307-3315.
[6]Ouyang S, Bao Z, Liao G S. Robust Recursive Least Squares Learning Algorithm for Principal Component Analysis [J]. IEEE Trans on NN, 2000,11(1): 215-221.
[7]Yang B. Projection Approximation Subspace Tracking[J]. IEEE Trans on SP,1995, 43(1): 95-107.
[8]Hasan E. An Efficient Algorithm for Rank and Subspace Tracking[J]. Mathematical and Computer Modelling, 2006, 44(7): 742-748.
[9]Xenofon G, George V. Fast and Stable Subspace Tracking[J]. IEEE Trans on SP, 2008, 56(4): 1452-1465.
[10]Roland B, Gael R, Bertrand D. Fast and Stable YAST Algorithm for Principal and Minor Subspace Tracking[J]. IEEE Tans on SP, 2008, 56(8): 3437-3446.
[11]Badeau R, David B, Richard G. Fast Approximated Power Iteration Subspace Tracking[J]. IEEE Trans on SP, 2005, 53(8): 2931-2941. |