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

页码：16 / 31

页数：16

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