Parameter-Free Weighted Multi-View Projected Clustering with Structured Graph Learning

Rong Wang, Feiping Nie, Zhen Wang, Haojie Hu, Xuelong Li

Research output: Contribution to journalArticlepeer-review

68 Scopus citations

Abstract

In many real-world applications, we are often confronted with high dimensional data which are represented by various heterogeneous views. How to cluster this kind of data is still a challenging problem due to the curse of dimensionality and effectively integration of different views. To address this problem, we propose two parameter-free weighted multi-view projected clustering methods which perform structured graph learning and dimensionality reduction simultaneously. We can use the obtained structured graph directly to extract the clustering indicators, without performing other discretization procedures as previous graph-based clustering methods have to do. Moreover, two parameter-free strategies are adopted to learn an optimal weight for each view automatically, without introducing a regularization parameter as previous methods do. Extensive experiments on several public datasets demonstrate that the proposed methods outperform other state-of-the-art approaches and can be used more practically.

Original languageEnglish
Article number8700233
Pages (from-to)2014-2025
Number of pages12
JournalIEEE Transactions on Knowledge and Data Engineering
Volume32
Issue number10
DOIs
StatePublished - 1 Oct 2020

Keywords

  • Multi-view clustering
  • dimensionality reduction
  • structured graph learning

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