Alyson K. Fletcher
Hybrid Approximate Message Passing with Applications to Structured Sparsity.
Fri, 11/11/2011 - 15:01 — SomNambulAuthors: Sundeep Rangan, Alyson K. Fletcher, Vivek K Goyal, Philip Schniter
Subjects: Information Theory
AbstractGaussian and quadratic approximations of message passing algorithms on graphs
have attracted considerable recent attention due to their computational
simplicity, analytic tractability, and wide applicability in optimization and
statistical inference problems. This paper presents a systematic framework for
incorporating such approximate message passing (AMP) methods in general
graphical models.Ranked Sparse Signal Support Detection.
Mon, 10/31/2011 - 15:01 — SomNambulAuthors: Sundeep Rangan, Alyson K. Fletcher, Vivek K Goyal
Subjects: Information Theory
AbstractThis paper considers the problem of detecting the support (sparsity pattern)
of a sparse vector from random noisy measurements. Conditional power of a
component of the sparse vector is defined as the energy conditioned on the
component being nonzero. Analysis of a simplified version of orthogonal
matching pursuit (OMP) called sequential OMP (SequOMP) demonstrates the
importance of knowledge of the rankings of conditional powers.Asymptotic Analysis of MAP Estimation via the Replica Method and Applications to Compressed Sensing.
Thu, 08/27/2009 - 13:23 — SomNambulAuthors: Sundeep Rangan, Alyson K. Fletcher, Vivek K Goyal
Subjects: Information Theory
AbstractThe replica method is a non-rigorous but widely-accepted technique from
statistical physics used in the asymptotic analysis of large, random, nonlinear
problems. This paper applies the replica method to non-Gaussian maximum a
posteriori (MAP) estimation. It is shown that with random linear measurements
and Gaussian noise, the asymptotic behavior of the MAP estimate of an
n-dimensional vector decouples as n scalar MAP estimators. The result is a
counterpart to Guo and Verdu's replica analysis of minimum mean-squared error
estimation.Asymptotic Analysis of MAP Estimation via the Replica Method and Applications to Compressed Sensing.
Thu, 08/27/2009 - 13:23 — SomNambulAuthors: Sundeep Rangan, Alyson K. Fletcher, Vivek K Goyal
Subjects: Information Theory
AbstractThe replica method is a non-rigorous but widely-accepted technique from
statistical physics used in the asymptotic analysis of large, random, nonlinear
problems. This paper applies the replica method to non-Gaussian maximum a
posteriori (MAP) estimation. It is shown that with random linear measurements
and Gaussian noise, the asymptotic behavior of the MAP estimate of an
n-dimensional vector decouples as n scalar MAP estimators. The result is a
counterpart to Guo and Verdu's replica analysis of minimum mean-squared error
estimation.
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