Riemannian joint dimensionality reduction and dictionary learning on symmetric positive definite manifolds

Hiroyuki Kasai, Bamdev Mishra

研究成果

1 被引用数 (Scopus)

抄録

Dictionary leaning (DL) and dimensionality reduction (DR) are powerful tools to analyze high-dimensional noisy signals. This paper presents a proposal of a novel Riemannian joint dimensionality reduction and dictionary learning (R-JDRDL) on symmetric positive definite (SPD) manifolds for classification tasks. The joint learning considers the interaction between dimensionality reduction and dictionary learning procedures by connecting them into a unified framework. We exploit a Riemannian optimization framework for solving DL and DR problems jointly. Finally, we demonstrate that the proposed R-JDRDL outperforms existing state-of-the-arts algorithms when used for image classification tasks.

本文言語English
ホスト出版物のタイトル2018 26th European Signal Processing Conference, EUSIPCO 2018
出版社European Signal Processing Conference, EUSIPCO
ページ2010-2014
ページ数5
ISBN(電子版)9789082797015
DOI
出版ステータスPublished - 2018 11 29
外部発表はい
イベント26th European Signal Processing Conference, EUSIPCO 2018 - Rome, Italy
継続期間: 2018 9 32018 9 7

出版物シリーズ

名前European Signal Processing Conference
2018-September
ISSN(印刷版)2219-5491

Conference

Conference26th European Signal Processing Conference, EUSIPCO 2018
国/地域Italy
CityRome
Period18/9/318/9/7

ASJC Scopus subject areas

  • 信号処理
  • 電子工学および電気工学

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