Spectral Embedding Fusion for Incomplete Multiview Clustering

Jie Chen, Yingke Chen, Zhu Wang, Haixian Zhang*, Xi Peng*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
41 Downloads (Pure)

Abstract

Incomplete multiview clustering (IMVC) aims to reveal the underlying structure of incomplete multiview data by partitioning data samples into clusters. Several graph-based methods exhibit a strong ability to explore high-order information among multiple views using low-rank tensor learning. However, spectral embedding fusion of multiple views is ignored in low-rank tensor learning. In addition, addressing missing instances or features is still an intractable problem for most existing IMVC methods. In this paper, we present a unified spectral embedding tensor learning (USETL) framework that integrates the spectral embedding fusion of multiple similarity graphs and spectral embedding tensor learning for IMVC. To remove redundant information from the original incomplete multiview data, spectral embedding fusion is performed by introducing spectral rotations at two different data levels, i.e., the spectral embedding feature level and the clustering indicator level. The aim of introducing spectral embedding tensor learning is to capture consistent and complementary information by seeking high-order correlations among multiple views. The strategy of removing missing instances is adopted to construct multiple similarity graphs for incomplete multiple views. Consequently, this strategy provides an intuitive and feasible way to construct multiple similarity graphs. Extensive experimental results on multiview datasets demonstrate the effectiveness of the two spectral embedding fusion methods within the USETL framework.

Original languageEnglish
Pages (from-to)4116-4130
Number of pages15
JournalIEEE Transactions on Image Processing
Volume33
DOIs
Publication statusPublished - 4 Jul 2024

Keywords

  • Clustering algorithms
  • Correlation
  • Filling
  • Incomplete multiview clustering
  • low-rank tensor learning
  • Matrix converters
  • Optimization
  • Sparse matrices
  • spectral embedding
  • spectral rotation
  • Tensors

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