Robust Multilinear Principal Component Analysis

被引：16

作者：

Inoue, Kohei ^{[1
]}

Hara, Kenji ^{[1
]}

Urahama, Kiichi ^{[1
]}

机构：

[1] Kyushu Univ, Dept Visual Commun Design, Minami Ku, Fukuoka 8158540, Japan

来源：

2009 IEEE 12TH INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV) | 2009年

关键词：

2-DIMENSIONAL PCA; ALGORITHM;

D O I：

10.1109/ICCV.2009.5459186

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：591 / 597

页数：7

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