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Multiview Ensemble Deep Generalized Eigenvalue Random Vector Functional Link
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DOI：10.1016/j.compeleceng.2026.111453.png)
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Abstract
En 中文
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MV-edGERVFL exploits complementary information from multiple views.
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Closed-form solutions enable efficient training and improved generalization.
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Kernel MV-edGERVFL handles nonlinear patterns through kernel-based mapping.
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Improved MV-edGERVFL mitigates singularity in correlated or sparse datasets.
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Experiments on UCI and AwA demonstrate competitive generalization of variants.
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