Approximation of N-Way Principal Component Analysis for Organ Data

被引:4
|
作者
Itoh, Hayato [1 ]
Imiya, Atsushi [2 ]
Sakai, Tomoya [3 ]
机构
[1] Chiba Univ, Sch Adv Integrat Sci, Chiba, Japan
[2] Chiba Univ, Inst Management & Informat Technol, Chiba, Japan
[3] Nagasaki Univ, Grad Sch Engn, Nagasaki, Japan
来源
COMPUTER VISION - ACCV 2016 WORKSHOPS, PT III | 2017年 / 10118卷
基金
日本学术振兴会;
关键词
D O I
10.1007/978-3-319-54526-4_2
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
We apply multilinear principal component analysis to dimension reduction and classification of human volumetric organ data, which are expressed as multiway array data. For the decomposition of multiway array data, tensor-based principal component analysis extracts multilinear structure of the data. We numerically clarify that low-pass filtering after the multidimensional discrete cosine transform efficiently approximates data dimension reduction procedure based on the tensor principal component analysis.
引用
收藏
页码:16 / 31
页数:16
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