TY - JOUR
T1 - L1-norm-based 2DPCA
AU - Li, Xuelong
AU - Pang, Yanwei
AU - Yuan, Yuan
PY - 2010/8
Y1 - 2010/8
N2 - In this paper, we first present a simple but effective L1-norm-based two-dimensional principal component analysis (2DPCA). Traditional L2-norm-based least squares criterion is sensitive to outliers, while the newly proposed L1-norm 2DPCA is robust. Experimental results demonstrate its advantages.
AB - In this paper, we first present a simple but effective L1-norm-based two-dimensional principal component analysis (2DPCA). Traditional L2-norm-based least squares criterion is sensitive to outliers, while the newly proposed L1-norm 2DPCA is robust. Experimental results demonstrate its advantages.
KW - L1 norm
KW - outlier
KW - subspace
KW - two-dimensional principal component analysis (2DPCA)
UR - http://www.scopus.com/inward/record.url?scp=77954762644&partnerID=8YFLogxK
U2 - 10.1109/TSMCB.2009.2035629
DO - 10.1109/TSMCB.2009.2035629
M3 - Article
C2 - 20083461
AN - SCOPUS:77954762644
SN - 1083-4419
VL - 40
SP - 1170
EP - 1175
JO - IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
JF - IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IS - 4
ER -