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

Yuto Ogino, Masahiro Yukawa

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

    Abstract

    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.

    Original languageEnglish
    Title of host publication2018 26th European Signal Processing Conference, EUSIPCO 2018
    PublisherEuropean Signal Processing Conference, EUSIPCO
    Pages1187-1191
    Number of pages5
    Volume2018-September
    ISBN (Electronic)9789082797015
    DOIs
    Publication statusPublished - 2018 Nov 29
    Event26th European Signal Processing Conference, EUSIPCO 2018 - Rome, Italy
    Duration: 2018 Sep 32018 Sep 7

    Other

    Other26th European Signal Processing Conference, EUSIPCO 2018
    CountryItaly
    CityRome
    Period18/9/318/9/7

    ASJC Scopus subject areas

    • Signal Processing
    • Electrical and Electronic Engineering

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  • Cite this

    Ogino, Y., & Yukawa, M. (2018). Spectral clustering with automatic cluster-number identification via finding sparse eigenvectors. In 2018 26th European Signal Processing Conference, EUSIPCO 2018 (Vol. 2018-September, pp. 1187-1191). [8553168] European Signal Processing Conference, EUSIPCO. https://doi.org/10.23919/EUSIPCO.2018.8553168