Multi-View Wasserstein Discriminant Analysis with Entropic Regularized Wasserstein Distance

Hiroyuki Kasai*

*この研究の対応する著者

研究成果: Conference contribution

5 被引用数 (Scopus)

抄録

Analysis of multi-view data has recently garnered growing attention because multi-view data frequently appear in real-world applications, which are collected or taken from many sources or captured using various sensors. A simple and popular promising approach is to learn a latent subspace shared by multi-view data. Nevertheless, because one sample lies in heterogeneous structure types, many existing multi-view data analyses show that discrepancies in within-class data across multiple views have a larger value than discrepancies within the same view from different views. To evaluate this discrepancy, this paper presents a proposal of a multi-view Wasserstein discriminant analysis, designated as MvWDA, which exploits a recently developed optimal transport theory. Numerical evaluations using three real-world datasets reveal the effectiveness of the proposed MvWDA.

本文言語English
ホスト出版物のタイトル2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ページ6039-6043
ページ数5
ISBN(電子版)9781509066315
DOI
出版ステータスPublished - 2020 5月
イベント2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Barcelona, Spain
継続期間: 2020 5月 42020 5月 8

出版物シリーズ

名前ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
2020-May
ISSN(印刷版)1520-6149

Conference

Conference2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
国/地域Spain
CityBarcelona
Period20/5/420/5/8

ASJC Scopus subject areas

  • ソフトウェア
  • 信号処理
  • 電子工学および電気工学

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