This article delves into the concept of sparsity in machine learning solutions within reproducing kernel Banach spaces (RKBS). It explores the explicit representer theorem for solutions in RKBS, focusing on the minimum norm interpolation (MNI) and regularization problems. The study establishes conditions for sparse kernel representations, emphasizing the role of the regularization parameter in promoting sparsity. Specific RKBSs, like sequence space ℓ1(N) and measure space, are identified to have sparse representer theorems. The content is structured as follows:
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by Rui Wang,Yue... at arxiv.org 03-08-2024
https://arxiv.org/pdf/2305.12584.pdfDeeper Inquiries