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.
['Kohei Inoue', 'Kenji Hara', 'Kiichi Urahama']
Robust multilinear principal component analysis