Department of Mathematics
 Search | Help | Login | pdf version | printable version

Math @ Duke



Publications [#243810] of Mauro Maggioni

Papers Published

  1. Allard, WK; Chen, G; Maggioni, M, Multi-scale geometric methods for data sets II: Geometric Multi-Resolution Analysis, Applied and Computational Harmonic Analysis, vol. 32 no. 3 (May, 2012), pp. 435-462, Elsevier BV, ISSN 1063-5203 [doi]
    (last updated on 2019/02/20)

    Data sets are often modeled as samples from a probability distribution in RD, for D large. It is often assumed that the data has some interesting low-dimensional structure, for example that of a d-dimensional manifold M, with d much smaller than D. When M is simply a linear subspace, one may exploit this assumption for encoding efficiently the data by projecting onto a dictionary of d vectors in RD (for example found by SVD), at a cost (n+D)d for n data points. When M is nonlinear, there are no "explicit" and algorithmically efficient constructions of dictionaries that achieve a similar efficiency: typically one uses either random dictionaries, or dictionaries obtained by black-box global optimization. In this paper we construct data-dependent multi-scale dictionaries that aim at efficiently encoding and manipulating the data. Their construction is fast, and so are the algorithms that map data points to dictionary coefficients and vice versa, in contrast with L1-type sparsity-seeking algorithms, but like adaptive nonlinear approximation in classical multi-scale analysis. In addition, data points are guaranteed to have a compressible representation in terms of the dictionary, depending on the assumptions on the geometry of the underlying probability distribution. © 2011 Elsevier Inc. All rights reserved.
ph: 919.660.2800
fax: 919.660.2821

Mathematics Department
Duke University, Box 90320
Durham, NC 27708-0320