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Updated: Jan 13, 2026

Decoding Natural Behavior from Neuroethological Embedding
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Nonnegative spectral embedding learning with adaptive neighbors for multi-view clustering.

Mingyu Zhao1, Feiping Nie1, Cong Wang2

  • 1School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi'an 710072, Shaanxi, PR China.

Neural Networks : the Official Journal of the International Neural Network Society
|January 11, 2026
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Summary

This study introduces Nonnegative Spectral Embedding with Adaptive Neighbors (NSEAN), a novel framework for multi-view clustering (MVC). NSEAN enhances robustness by adaptively learning similarity graphs and reducing hyperparameter sensitivity.

Keywords:
Adaptive graph learningGraph reconstructionMulti-view clusteringSpectral embedding

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Area of Science:

  • Machine Learning
  • Data Mining
  • Computer Science

Background:

  • Traditional graph-based multi-view clustering (MVC) methods struggle with fixed similarity graphs and numerous sensitive hyperparameters.
  • This limits their robustness and practical application in real-world scenarios.

Purpose of the Study:

  • To develop a unified, one-stage MVC framework that overcomes the limitations of existing methods.
  • Introduce Nonnegative Spectral Embedding with Adaptive Neighbors (NSEAN) for improved clustering performance and robustness.

Main Methods:

  • NSEAN integrates per-view adaptive graph learning with nonnegative spectral embedding in a single stage.
  • It jointly learns adaptive similarity graphs and a consensus spectral embedding, eliminating post-processing.
  • An Augmented Lagrangian Multiplier (ALM) strategy is used for efficient optimization of coupled constraints.

Main Results:

  • NSEAN demonstrates competitive or superior clustering performance across various real-world multi-view datasets.
  • The method requires only one hyperparameter (k, number of neighbors), to which it is empirically insensitive.
  • This significantly reduces the need for cumbersome hyperparameter tuning.

Conclusions:

  • NSEAN offers a robust and practical solution for multi-view clustering.
  • Its adaptive graph learning and simplified parameter tuning make it highly applicable.
  • The framework provides interpretable cluster assignments through enforced nonnegativity and orthogonality.