6,262 research outputs found
Spectrum-Adapted Tight Graph Wavelet and Vertex-Frequency Frames
We consider the problem of designing spectral graph filters for the
construction of dictionaries of atoms that can be used to efficiently represent
signals residing on weighted graphs. While the filters used in previous
spectral graph wavelet constructions are only adapted to the length of the
spectrum, the filters proposed in this paper are adapted to the distribution of
graph Laplacian eigenvalues, and therefore lead to atoms with better
discriminatory power. Our approach is to first characterize a family of systems
of uniformly translated kernels in the graph spectral domain that give rise to
tight frames of atoms generated via generalized translation on the graph. We
then warp the uniform translates with a function that approximates the
cumulative spectral density function of the graph Laplacian eigenvalues. We use
this approach to construct computationally efficient, spectrum-adapted, tight
vertex-frequency and graph wavelet frames. We give numerous examples of the
resulting spectrum-adapted graph filters, and also present an illustrative
example of vertex-frequency analysis using the proposed construction
Compressed Sensing and Redundant Dictionaries
This article extends the concept of compressed sensing to signals that are
not sparse in an orthonormal basis but rather in a redundant dictionary. It is
shown that a matrix, which is a composition of a random matrix of certain type
and a deterministic dictionary, has small restricted isometry constants. Thus,
signals that are sparse with respect to the dictionary can be recovered via
Basis Pursuit from a small number of random measurements. Further, thresholding
is investigated as recovery algorithm for compressed sensing and conditions are
provided that guarantee reconstruction with high probability. The different
schemes are compared by numerical experiments.Comment: error in a constant correcte
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