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

Hiroyuki Kasai, Bamdev Mishra

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2018 26th European Signal Processing Conference, EUSIPCO 2018
PublisherEuropean Signal Processing Conference, EUSIPCO
Pages2010-2014
Number of pages5
ISBN (Electronic)9789082797015
DOIs
Publication statusPublished - 2018 Nov 29
Externally publishedYes
Event26th European Signal Processing Conference, EUSIPCO 2018 - Rome, Italy
Duration: 2018 Sep 32018 Sep 7

Publication series

NameEuropean Signal Processing Conference
Volume2018-September
ISSN (Print)2219-5491

Conference

Conference26th European Signal Processing Conference, EUSIPCO 2018
CountryItaly
CityRome
Period18/9/318/9/7

Keywords

  • Dictionary leaning
  • Dimensionality reduction
  • Riemannian manifold
  • SPD matrix

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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  • Cite this

    Kasai, H., & Mishra, B. (2018). Riemannian joint dimensionality reduction and dictionary learning on symmetric positive definite manifolds. In 2018 26th European Signal Processing Conference, EUSIPCO 2018 (pp. 2010-2014). [8553200] (European Signal Processing Conference; Vol. 2018-September). European Signal Processing Conference, EUSIPCO. https://doi.org/10.23919/EUSIPCO.2018.8553200