SUPERVISED CANONICAL CORRELATION ANALYSIS OF DATA ON SYMMETRIC POSITIVE DEFINITE MANIFOLDS BY RIEMANNIAN DIMENSIONALITY REDUCTION

被引:0
作者
Fallah, Faezeh [1 ]
Yang, Bin [1 ]
机构
[1] Univ Stuttgart, Inst Signal Proc & Syst Theory, Stuttgart, Germany
来源
2020 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING | 2020年
关键词
Canonical correlation analysis; Classification; Riemannian dimensionality reduction; Stiefel manifold; ADMM optimization; GEOMETRY;
D O I
10.1109/icassp40776.2020.9054243
中图分类号
O42 [声学];
学科分类号
070206 ; 082403 ;
摘要
Most computer vision problems entail data that reside on Riemannian manifolds. Canonical correlation analysis (CCA) is a powerful method that captures correlations between any two sets of matrices. In this paper, we propose a framework for a supervised CCA of manifold-based data. This framework aims to find the optimal dimensionality reduction maps that maximize the discriminative power of any classifier in the reduced dimensional space and the correlation between the projected sets. This allows to incorporate the CCA into a classifier that analyzes multichannel or multimodal data on separate manifolds. The proposed method is evaluated on the challenging task of segmenting cardiac adipose tissues on fat-water (2-channel) magnetic resonance images.
引用
收藏
页码:8369 / 8373
页数:5
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