214 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
Regression in random design and Bayesian warped wavelets estimators
In this paper we deal with the regression problem in a random design setting.
We investigate asymptotic optimality under minimax point of view of various
Bayesian rules based on warped wavelets and show that they nearly attain
optimal minimax rates of convergence over the Besov smoothness class
considered. Warped wavelets have been introduced recently, they offer very good
computable and easy-to-implement properties while being well adapted to the
statistical problem at hand. We particularly put emphasis on Bayesian rules
leaning on small and large variance Gaussian priors and discuss their
simulation performances comparing them with a hard thresholding procedure
Adaptive Nonparametric Regression on Spin Fiber Bundles
The construction of adaptive nonparametric procedures by means of wavelet
thresholding techniques is now a classical topic in modern mathematical
statistics. In this paper, we extend this framework to the analysis of
nonparametric regression on sections of spin fiber bundles defined on the
sphere. This can be viewed as a regression problem where the function to be
estimated takes as its values algebraic curves (for instance, ellipses) rather
than scalars, as usual. The problem is motivated by many important
astrophysical applications, concerning for instance the analysis of the weak
gravitational lensing effect, i.e. the distortion effect of gravity on the
images of distant galaxies. We propose a thresholding procedure based upon the
(mixed) spin needlets construction recently advocated by Geller and Marinucci
(2008,2010) and Geller et al. (2008,2009), and we investigate their rates of
convergence and their adaptive properties over spin Besov balls.Comment: 40 page
Mixed Needlets
The construction of needlet-type wavelets on sections of the spin line
bundles over the sphere has been recently addressed in Geller and Marinucci
(2008), and Geller et al. (2008,2009). Here we focus on an alternative proposal
for needlets on this spin line bundle, in which needlet coefficients arise from
the usual, rather than the spin, spherical harmonics, as in the previous
constructions. We label this system mixed needlets and investigate in full
their properties, including localization, the exact tight frame
characterization, reconstruction formula, decomposition of functional spaces,
and asymptotic uncorrelation in the stochastic case. We outline astrophysical
applications.Comment: 26 page
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