Consistency-aware and inconsistency-aware graph-based multi-view clustering

Mitsuhiko Horie, Hiroyuki Kasai

研究成果: Conference contribution

抄録

Multi-view data analysis has gained increasing popularity because multi-view data are frequently encountered in machine learning applications. A simple but promising approach for clustering of multi-view data is multi-view clustering (MVC), which has been developed extensively to classify given subjects into some clustered groups by learning latent common features that are shared across multi-view data. Among existing approaches, graph-based multi-view clustering (GMVC) achieves state-of-the-art performance by leveraging a shared graph matrix called the unified matrix. However, existing methods including GMVC do not explicitly address inconsistent parts of input graph matrices. Consequently, they are adversely affected by unacceptable clustering performance. To this end, this paper proposes a new GMVC method that incorporates consistent and inconsistent parts lying across multiple views. This proposal is designated as CI-GMVC. Numerical evaluations of real-world datasets demonstrate the effectiveness of the proposed CI-GMVC.

本文言語English
ホスト出版物のタイトル28th European Signal Processing Conference, EUSIPCO 2020 - Proceedings
出版社European Signal Processing Conference, EUSIPCO
ページ1472-1476
ページ数5
ISBN(電子版)9789082797053
DOI
出版ステータスPublished - 2021 1 24
イベント28th European Signal Processing Conference, EUSIPCO 2020 - Amsterdam, Netherlands
継続期間: 2020 8 242020 8 28

出版物シリーズ

名前European Signal Processing Conference
2021-January
ISSN(印刷版)2219-5491

Conference

Conference28th European Signal Processing Conference, EUSIPCO 2020
国/地域Netherlands
CityAmsterdam
Period20/8/2420/8/28

ASJC Scopus subject areas

  • 信号処理
  • 電子工学および電気工学

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