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CORR
2010
Springer

Learning Functions of Few Arbitrary Linear Parameters in High Dimensions

13 years 10 months ago
Learning Functions of Few Arbitrary Linear Parameters in High Dimensions
Let us assume that f is a continuous function defined on the unit ball of Rd , of the form f(x) = g(Ax), where A is a k×d matrix and g is a function of k variables for k ≪ d. We are given a budget m ∈ N of possible point evaluations f(xi), i = 1, . . . , m, of f, which we are allowed to query in order to construct a uniform approximating function. Under certain smoothness and variation assumptions on the function g, and an arbitrary choice of the matrix A, we present in this paper
Massimo Fornasier, Karin Schnass, Jan Vybír
Added 24 Jan 2011
Updated 24 Jan 2011
Type Journal
Year 2010
Where CORR
Authors Massimo Fornasier, Karin Schnass, Jan Vybíral
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