Clustering quality metrics for subspace clustering

PATTERN RECOGNITION

卷:104

Article

Lipor, John¹  Balzano, Laura² 

[1] Portland State Univ, Dept Elect & Comp Engn, 1900 SW 4th Ave,Suite 160-11, Portland, OR 97201 USA

[2] Univ Michigan, Dept Elect & Comp Engn, 1301 Beal Ave, Ann Arbor, MI 48109 USA

关键词: Subspace clustering;    Clustering validation;    Union of subspaces;   

DOI  :  10.1016/j.patcog.2020.107328

来源: Elsevier

【 摘 要 】

We study the problem of clustering validation, i.e., clustering evaluation without knowledge of ground-truth labels, for the increasingly-popular framework known as subspace clustering. Existing clustering quality metrics (CQMs) rely heavily on a notion of distance between points, but common metrics fail to capture the geometry of subspace clustering. We propose a novel point-to-point pseudometric for points lying on a union of subspaces and show how this allows for the application of existing CQMs to the subspace clustering problem. We provide theoretical and empirical justification for the proposed point-to-point distance, and then demonstrate on a number of common benchmark datasets that our proposed methods generally outperform existing graph-based CQMs in terms of choosing the best clustering and the number of clusters. (C) 2020 Elsevier Ltd. All rights reserved.

【 授权许可】

Free