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Dimension- adaptive latent representation learning with normalized hyperbolic tensor rank for multi-view clustering.

Gang Zhu1, Lixin Han1, Hong Yan2

  • 1College of Computer Science and Software Engineering, Hohai University, Nanjing,211100, China.

Neural Networks : the Official Journal of the International Neural Network Society
|April 3, 2026
PubMed
Summary

This study introduces Dimension-adaptive Latent Representation Learning with Normalized Hyperbolic Tensor Rank (DLRL-NHTR) for multi-view clustering. The novel method improves clustering by adaptively selecting latent dimensions and capturing complex correlations within and between data views.

Keywords:
Dimension-adaptive latent representationElastic structural regularizationMulti-view clusteringNormalized hyperbolic tensor rank

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Area of Science:

  • Machine Learning
  • Data Mining
  • Artificial Intelligence

Background:

  • Latent representation methods are crucial for multi-view clustering, extracting reliable features from raw data.
  • Existing methods often require manual latent space dimension selection, lacking theoretical grounding.
  • Current approaches inadequately capture high-order correlations between views and within-view structural features.

Purpose of the Study:

  • To propose a novel dimension-adaptive latent representation learning method for multi-view clustering.
  • To overcome limitations of fixed latent dimension selection and improve the capture of inter-view and intra-view correlations.
  • To enhance the overall performance of multi-view clustering algorithms.

Main Methods:

  • Introduced a dimension-adaptive latent representation framework using zero-space projection, eliminating manual dimension selection.
  • Employed an improved Normalized Hyperbolic Tensor Rank (NHTR) to effectively model high-order correlations across multiple views.
  • Incorporated an Elastic Structural Regularization (ESR) term to capture multi-level structural information within individual views.

Main Results:

  • The proposed Dimension-adaptive Latent Representation Learning with Normalized Hyperbolic Tensor Rank (DLRL-NHTR) demonstrated superior clustering performance.
  • Experiments on ten diverse benchmark datasets showed significant improvements compared to 12 state-of-the-art methods.
  • The method effectively addressed limitations in latent dimension selection and feature representation.

Conclusions:

  • DLRL-NHTR offers a robust and theoretically grounded approach to multi-view clustering.
  • The adaptive dimension selection and enhanced correlation modeling significantly boost clustering accuracy.
  • The method provides a powerful tool for complex multi-view data analysis.