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Tensorized Incomplete Multi-view Kernel Subspace Clustering.

Guang-Yu Zhang1, Dong Huang1, Chang-Dong Wang2

  • 1College of Mathematics and Informatics, South China Agricultural University, China.

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|July 28, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a Tensorized Incomplete Multi-view Kernel Subspace Clustering (TIMKSC) method to address limitations in current incomplete multi-view clustering research. TIMKSC effectively recovers nonlinear structures and high-order relationships, offering a more practical solution with fewer hyperparameters.

Keywords:
Kernelized modelMulti-view incomplete clusteringTensor subspace clusteringUnified framework

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Area of Science:

  • Machine Learning
  • Data Mining
  • Computer Vision

Background:

  • Incomplete Multi-view Clustering (IMC) research has advanced, but current methods struggle with nonlinear subspace structures, high-order relationships, and excessive hyperparameters.
  • Existing IMC approaches often fail to capture complex patterns within multiple kernel spaces and neglect inter-representation correlations.

Purpose of the Study:

  • To propose a novel Tensorized Incomplete Multi-view Kernel Subspace Clustering (TIMKSC) approach to overcome the limitations of existing IMC methods.
  • To enhance the robustness and practicality of incomplete multi-view clustering by addressing nonlinear structure recovery, high-order relationship modeling, and hyperparameter complexity.

Main Methods:

  • Developed a TIMKSC approach integrating kernel learning with an incomplete subspace clustering framework.
  • Employed tensorization to impute incomplete kernel matrices and learn low-rank tensor representations in a mutually reinforcing manner.
  • Designed an efficient three-step algorithm to minimize the unified objective function, involving only one hyperparameter.

Main Results:

  • The TIMKSC approach successfully recovers latent subspace structures from multiple views, even with incomplete data.
  • It effectively captures high-order correlations among observed and missing samples, improving subspace clustering performance.
  • Experimental results on benchmark datasets demonstrate the superior performance of the proposed TIMKSC method compared to existing approaches.

Conclusions:

  • The TIMKSC method provides a robust and practical solution for incomplete multi-view clustering by addressing key challenges in nonlinear structure recovery and high-order relationship modeling.
  • The proposed approach offers a significant improvement in clustering accuracy and efficiency, with reduced hyperparameter tuning requirements.
  • The availability of source code and datasets facilitates further research and application of this advanced IMC technique.