Spectral clustering with automatic cluster-number identification via finding sparse eigenvectors

Yuto Ogino, Masahiro Yukawa

研究成果: Conference contribution

抄録

Spectral clustering is an empirically successful approach to separating a dataset into some groups with possibly complex shapes based on pairwise affinity. Identifying the number of clusters automatically is still an open issue, although many heuristics have been proposed. In this paper, imposing sparsity on the eigenvectors of graph Laplacian is proposed to attain reasonable approximations of the so-called cluster-indicator-vectors, from which the clusters as well as the cluster number are identified. The proposed algorithm enjoys low computational complexity as it only computes a relevant subset of eigenvectors. It also enjoys better clustering quality than the existing methods, as shown by simulations using nine real datasets.

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1187-1191
ページ数5
ISBN(電子版)9789082797015
DOI
出版ステータスPublished - 2018 11月 29
イベント26th European Signal Processing Conference, EUSIPCO 2018 - Rome, Italy
継続期間: 2018 9月 32018 9月 7

出版物シリーズ

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

Other

Other26th 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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