We propose a new method for few-sample supervised feature selection (FS). Our method first learns the manifold of the feature space of each class using kernels capturing multi-feature associations. Then, based on Riemannian geometry, a composite kernel is computed, extracting the differences between the learned feature associations. Finally, a FS score based on spectral analysis is computed.
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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.
DavidCohen2/ManiFeSt
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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.
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