Adaptive Weighted LSSVM for Multi-View Classification

• URL: http://arxiv.org/abs/2512.02653v1
• Date: Tue, 02 Dec 2025 11:14:47 GMT
• Title: Adaptive Weighted LSSVM for Multi-View Classification
• Authors: Farnaz Faramarzi Lighvan, Mehrdad Asadi, Lynn Houthuys,
• Abstract summary: AW-LSSVM promotes complementary learning by an iterative global coupling to make each view focus on hard samples of others from previous iterations.<n>Experiments demonstrate AW-LSSVM outperforms existing kernel-based multi-view methods on most datasets.
• Score: 0.5161531917413708
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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