Enhancing Multiview Synergy: Robust Learning by Exploiting the Wave Loss Function with Consensus and Complementarity Principles

TL;DR

This paper introduces Wave-MvSVM, a multiview learning framework that leverages a novel wave loss function to exploit both consensus and complementarity principles, improving robustness and performance in noisy, multiview datasets.

Contribution

The paper proposes Wave-MvSVM, integrating a new wave loss function with view co-regularization and adaptive weighting, addressing limitations of existing multiview SVMs by enhancing robustness and leveraging complementarity.

Findings

Wave-MvSVM outperforms existing models on diverse datasets.

The wave loss function effectively mitigates noise and outliers.

Theoretical analysis confirms improved generalization capabilities.

Abstract

Multiview learning (MvL) is an advancing domain in machine learning, leveraging multiple data perspectives to enhance model performance through view-consistency and view-discrepancy. Despite numerous successful multiview-based SVM models, existing frameworks predominantly focus on the consensus principle, often overlooking the complementarity principle. Furthermore, they exhibit limited robustness against noisy, error-prone, and view-inconsistent samples, prevalent in multiview datasets. To tackle the aforementioned limitations, this paper introduces Wave-MvSVM, a novel multiview support vector machine framework leveraging the wave loss (W-loss) function, specifically designed to harness both consensus and complementarity principles. Unlike traditional approaches that often overlook the complementary information among different views, the proposed Wave-MvSVM ensures a more comprehensive and resilient learning process by integrating both principles effectively. The W-loss function, characterized by its smoothness, asymmetry, and bounded nature, is particularly effective in mitigating the adverse effects of noisy and outlier data, thereby enhancing model stability. Theoretically, the W-loss function also exhibits a crucial classification-calibrated property, further boosting its effectiveness. Wave-MvSVM employs a between-view co-regularization term to enforce view consistency and utilizes an adaptive combination weight strategy to maximize the discriminative power of each view. The optimization problem is efficiently solved using a combination of GD and the ADMM, ensuring reliable convergence to optimal solutions. Theoretical analyses, grounded in Rademacher complexity, validate the generalization capabilities of the Wave-MvSVM model. Extensive empirical evaluations across diverse datasets demonstrate the superior performance of Wave-MvSVM in comparison to existing benchmark models.