A comparative study of supervised learning algorithms for symmetric positive definite features

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Abstract

In recent years, the use of Riemannian geometry has reportedly shown an increased performance for machine learning problems whose features lie in the symmetric positive definite (SPD) manifold. The present paper aims at reviewing several approaches based on this paradigm and provide a reproducible comparison of their output on a classic learning task of pedestrian detection. Notably, the robustness of these approaches to corrupted data will be assessed.

Original languageEnglish
Title of host publication28th European Signal Processing Conference, EUSIPCO 2020 - Proceedings
PublisherEURASIP
Pages950-954
Number of pages5
ISBN (Electronic)9789082797053
DOIs
Publication statusPublished - 2020
MoE publication typeA4 Article in a conference publication
EventEuropean Signal Processing Conference - Amsterdam, Netherlands
Duration: 24 Aug 202028 Aug 2020

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491
ISSN (Electronic)2076-1465

Conference

ConferenceEuropean Signal Processing Conference
Abbreviated titleEUSIPCO
CountryNetherlands
CityAmsterdam
Period24/08/202028/08/2020

Keywords

  • Covariance matrix
  • Pedestrian detection
  • Riemannian geometry
  • Supervised learning

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