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Near Optimal Signal Recovery From Random Projections:Universal Encoding Strategies?
Random matrices singular values of random matrices signal recovery random projections concentration of measure sparsity trigonometric expansions uncertainty principle convex optimization duality in optimization linear programming
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2015/6/17
Suppose we are given a vector f in a class F ⊂ RN, e.g. a class of digital signals or digital images. How many linear measurements do we need to make about f to be able to recover f to within pr...
Practical Signal Recovery from Random Projections
Signal Recovery Random Projections
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2015/6/17
Can we recover a signal f ∈ RN from a small number of linear measurements? A series of recent papers developed a collection of results showing that it is surprisingly possible to reconstruct certain t...
Templates for Convex Cone Problems with Applications to Sparse Signal Recovery
Optimal first-order methods Nesterov’s accelerated descent algorithms proximal algorithms conic duality smoothing by conjugation the Dantzig selector the LASSO nuclearnorm minimization
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2015/6/17
This paper develops a general framework for solving a variety of convex cone problems that frequently arise in signal processing, machine learning, statistics, and other fields. The approach works as ...
PhaseLift: Exact and Stable Signal Recovery from Magnitude Measurements via Convex Programming
Exact and Stable Signal Recovery Magnitude Measurements Convex Programming
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2015/6/17
Suppose we wish to recover a signal x ∈ Cn from m intensity measurements of the form |hx, zii|2, i = 1, 2, . . . , m; that is, from data in which phase information is missing. We prove that if the vec...