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Sparse Representer Theorems for Learning in Reproducing Kernel Banach Spaces
March 8, 2024, 5:43 a.m. | Rui Wang, Yuesheng Xu, Mingsong Yan
cs.LG updates on arXiv.org arxiv.org
Abstract: Sparsity of a learning solution is a desirable feature in machine learning. Certain reproducing kernel Banach spaces (RKBSs) are appropriate hypothesis spaces for sparse learning methods. The goal of this paper is to understand what kind of RKBSs can promote sparsity for learning solutions. We consider two typical learning models in an RKBS: the minimum norm interpolation (MNI) problem and the regularization problem. We first establish an explicit representer theorem for solutions of these problems, …
abstract arxiv cs.lg feature hypothesis kernel kind machine machine learning math.fa paper promote solution solutions spaces sparsity type
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