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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.02653 |
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| _version_ | 1866911297961984000 |
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| author | Lighvan, Farnaz Faramarzi Asadi, Mehrdad Houthuys, Lynn |
| author_facet | Lighvan, Farnaz Faramarzi Asadi, Mehrdad Houthuys, Lynn |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_02653 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Adaptive Weighted LSSVM for Multi-View Classification Lighvan, Farnaz Faramarzi Asadi, Mehrdad Houthuys, Lynn Machine Learning 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. |
| title | Adaptive Weighted LSSVM for Multi-View Classification |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2512.02653 |