Analysis of Multilinear Subspaces Based on Geodesic Distance

被引：2

作者：

Itoh, Hayato ^{[1
,2
]}

Imiya, Atsushi ^{[3
]}

Sakai, Tomoya ^{[4
]}

机构：

[1] Nagoya Univ, Grad Sch Informat, Nagoya, Aichi, Japan

[2] Chiba Univ, Grad Sch Adv Integrat Sci, Chiba, Japan

[3] Chiba Univ, Inst Management & Informat Technol, Chiba, Japan

[4] Nagasaki Univ, Grad Sch Engn, Nagasaki, Japan

来源：

COMPUTER ANALYSIS OF IMAGES AND PATTERNS | 2017年 / 10424卷

关键词：

GRASSMANN MANIFOLDS; GEOMETRY; ALGORITHMS; ANGLES; MODELS;

D O I：

10.1007/978-3-319-64689-3_31

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Tensor principal component analysis enables the efficient analysis of spatial textures of volumetric images and spatio-temporal changes of volumetric video sequences. To extend the subspace methods for analysis of linear subspaces, we are required to quantitatively evaluate the differences between multilinear subspaces. This discrimination of multilinear subspaces is achieved by computing the geodesic distance between tensor subspaces.

引用

页码：384 / 396

页数：13

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