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Generalization Guarantees for Sparse Kernel Approximation with Entropic Optimal Features
Liang Ding · Rui Tuo · Shahin Shahrampour

Tue Jul 14 09:00 AM -- 09:45 AM & Tue Jul 14 08:00 PM -- 08:45 PM (PDT) @ None #None
Despite their success, kernel methods suffer from a massive computational cost in practice. In this paper, in lieu of commonly used kernel expansion with respect to $N$ inputs, we develop a novel optimal design maximizing the entropy among kernel features. This procedure results in a kernel expansion with respect to entropic optimal features (EOF), improving the data representation dramatically due to features dissimilarity. Under mild technical assumptions, our generalization bound shows that with only $O(N^{\frac{1}{4}})$ features (disregarding logarithmic factors), we can achieve the optimal statistical accuracy (i.e., $O(1/\sqrt{N})$). The salient feature of our design is its sparsity that significantly reduces the time and space costs. Our numerical experiments on benchmark datasets verify the superiority of EOF over the state-of-the-art in kernel approximation.

Author Information

Liang Ding (Texas A&M University)
Rui Tuo (Texas A&M University)
Shahin Shahrampour (Texas A&M University)

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