TY - GEN
T1 - Robust multilinear principal component analysis
AU - Inoue, Kohei
AU - Hara, Kenji
AU - Urahama, Kiichi
PY - 2009/12/1
Y1 - 2009/12/1
N2 - We propose two methods for robustifying multilinear principal component analysis (MPCA) which is an extension of the conventional PCA for reducing the dimensions of vectors to higher-order tensors. For two kinds of outliers, i.e., sample outliers and intra-sample outliers, we derive iterative algorithms on the basis of the Lagrange multipliers. We also demonstrate that the proposed methods outperform the original MPCA when datasets contain such outliers experimentally.
AB - We propose two methods for robustifying multilinear principal component analysis (MPCA) which is an extension of the conventional PCA for reducing the dimensions of vectors to higher-order tensors. For two kinds of outliers, i.e., sample outliers and intra-sample outliers, we derive iterative algorithms on the basis of the Lagrange multipliers. We also demonstrate that the proposed methods outperform the original MPCA when datasets contain such outliers experimentally.
UR - http://www.scopus.com/inward/record.url?scp=77953191395&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77953191395&partnerID=8YFLogxK
U2 - 10.1109/ICCV.2009.5459186
DO - 10.1109/ICCV.2009.5459186
M3 - Conference contribution
AN - SCOPUS:77953191395
SN - 9781424444205
T3 - Proceedings of the IEEE International Conference on Computer Vision
SP - 591
EP - 597
BT - 2009 IEEE 12th International Conference on Computer Vision, ICCV 2009
T2 - 12th International Conference on Computer Vision, ICCV 2009
Y2 - 29 September 2009 through 2 October 2009
ER -