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    Capturing Ridge Functions in High Dimensions from Point Queries

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    Type
    Article
    Authors
    Cohen, Albert
    Daubechies, Ingrid
    DeVore, Ronald
    Kerkyacharian, Gerard
    Picard, Dominique
    KAUST Grant Number
    KUS-C1-016-04
    Date
    2011-12-21
    Online Publication Date
    2011-12-21
    Print Publication Date
    2012-04
    Permanent link to this record
    http://hdl.handle.net/10754/597725
    
    Metadata
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    Abstract
    Constructing a good approximation to a function of many variables suffers from the "curse of dimensionality". Namely, functions on ℝ N with smoothness of order s can in general be captured with accuracy at most O(n -s/N) using linear spaces or nonlinear manifolds of dimension n. If N is large and s is not, then n has to be chosen inordinately large for good accuracy. The large value of N often precludes reasonable numerical procedures. On the other hand, there is the common belief that real world problems in high dimensions have as their solution, functions which are more amenable to numerical recovery. This has led to the introduction of models for these functions that do not depend on smoothness alone but also involve some form of variable reduction. In these models it is assumed that, although the function depends on N variables, only a small number of them are significant. Another variant of this principle is that the function lives on a low dimensional manifold. Since the dominant variables (respectively the manifold) are unknown, this leads to new problems of how to organize point queries to capture such functions. The present paper studies where to query the values of a ridge function f(x)=g(a · x) when both a∈ℝ N and g ∈ C[0,1] are unknown. We establish estimates on how well f can be approximated using these point queries under the assumptions that g ∈ C s[0,1]. We also study the role of sparsity or compressibility of a in such query problems. © 2011 Springer Science+Business Media, LLC.
    Citation
    Cohen A, Daubechies I, DeVore R, Kerkyacharian G, Picard D (2011) Capturing Ridge Functions in High Dimensions from Point Queries. Constructive Approximation 35: 225–243. Available: http://dx.doi.org/10.1007/s00365-011-9147-6.
    Sponsors
    This research was supported by the Office of Naval Research Contracts ONR-N00014-08-1-1113, ONR N00014-09-1-0107; the AFOSR Contract FA95500910500; the ARO/DoD Contract W911NF-07-1-0185; the NSF Grant DMS 0915231; the French-German PROCOPE contract 11418YB; the Agence Nationale de la Recherche (ANR) project ECHANGE (ANR-08-EMER-006); the excellence chair of the Fondation "Sciences Mathematiques de Paris" held by Ronald DeVore. This publication is based on work supported by Award No. KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST).
    Publisher
    Springer Nature
    Journal
    Constructive Approximation
    DOI
    10.1007/s00365-011-9147-6
    ae974a485f413a2113503eed53cd6c53
    10.1007/s00365-011-9147-6
    Scopus Count
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