The paper contrasts evaluating a conventional kernel expansion, which requires O(Nd) operations and retaining much of the dataset unless sparse, with evaluating a learned linear hyperplane on the proposed random features, which it states requires O(D+d) operations and storage. · CiteArk
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The paper contrasts evaluating a conventional kernel expansion, which requires O(Nd) operations and retaining much of the dataset unless sparse, with evaluating a learned linear hyperplane on the proposed random features, which it states requires O(D+d) operations and storage.
Source: paper:PDF p. 2, Section 1, second paragraph
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