We propose a novel few-sample supervised feature selection (FS) method. It learns class-specific feature space manifolds using multi-feature association kernels. The composite kernel captures differences in learned associations, and a spectral-based FS score is derived using Riemannian geometry.
active 2023-11-16 → 2024-04-23 (UTC)
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| Contributor | Contributions | Pushes | PRs | Comments |
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| Heilong623 | 1 | 0 | 0 | 0 |
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