Adaptive Weighted LSSVM for Multi-View Classification

TL;DR

This paper introduces AW-LSSVM, an adaptive weighted multi-view learning method that enhances collaboration among views through iterative coupling, leading to improved classification performance while preserving data privacy.

Contribution

The paper proposes a novel adaptive weighted LS-SVM that promotes complementary learning via iterative global coupling, addressing limitations of existing multi-view methods.

Findings

AW-LSSVM outperforms existing kernel-based multi-view methods on most datasets.

It maintains raw feature isolation, suitable for privacy-preserving scenarios.

Demonstrates effective collaboration among views through iterative coupling.

Abstract

Multi-view learning integrates diverse representations of the same instances to improve performance. Most existing kernel-based multi-view learning methods use fusion techniques without enforcing an explicit collaboration type across views or co-regularization which limits global collaboration. We propose AW-LSSVM, an adaptive weighted LS-SVM that promotes complementary learning by an iterative global coupling to make each view focus on hard samples of others from previous iterations. Experiments demonstrate that AW-LSSVM outperforms existing kernel-based multi-view methods on most datasets, while keeping raw features isolated, making it also suitable for privacy-preserving scenarios.