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Enhancing multiview synergy: Robust learning by exploiting the wave loss function with consensus and complementarity

A Quadir1, Mushir Akhtar1, M Tanveer1

  • 1Department of Mathematics, Indian Institute of Technology Indore, Simrol, Indore, 453552, India.

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|April 9, 2025
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Summary
This summary is machine-generated.

This study introduces Wave-MvSVM, a new multiview support vector machine (SVM) model that uses wave loss to effectively combine consensus and complementarity principles. Wave-MvSVM demonstrates improved robustness and performance on diverse datasets, outperforming existing multiview learning methods.

Keywords:
ADMM algorithmClassification-calibrationConsensus and complementary informationMultiview learning (MvL)Rademacher complexityWave loss function

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Area of Science:

  • Machine Learning
  • Pattern Recognition
  • Data Science

Background:

  • Multiview learning (MvL) enhances models using multiple data perspectives, focusing on view-consistency and view-discrepancy.
  • Existing multiview support vector machine (SVM) models primarily use the consensus principle, often neglecting complementarity and lacking robustness against noisy data.

Purpose of the Study:

  • To introduce Wave-MvSVM, a novel MvL framework that integrates both consensus and complementarity principles.
  • To enhance model robustness against noisy, error-prone, and view-inconsistent samples in multiview datasets.
  • To leverage the unique properties of the wave loss (W-loss) function for improved learning.

Main Methods:

  • The proposed Wave-MvSVM framework utilizes a novel wave loss (W-loss) function, characterized by smoothness, asymmetry, and boundedness.
  • It incorporates a between-view co-regularization term for view consistency and an adaptive combination weight strategy.
  • Optimization is achieved through a combination of gradient descent (GD) and alternating direction method of multipliers (ADMM).

Main Results:

  • Wave-MvSVM effectively harnesses both consensus and complementarity principles, offering a more comprehensive learning process.
  • The W-loss function significantly mitigates the impact of noisy and outlier data, enhancing model stability and classification calibration.
  • Empirical evaluations across diverse datasets show Wave-MvSVM outperforms existing benchmark models, demonstrating superior generalization ability.

Conclusions:

  • Wave-MvSVM presents a robust and efficient solution for multiview learning challenges, effectively addressing limitations of previous approaches.
  • The model's efficacy is further validated through its implementation on a Schizophrenia dataset, showcasing real-world applicability.
  • Theoretical generalization is supported by Rademacher complexity analysis.