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Sparse signal representations and approximations from overcomplete dictionaries have become an invaluable tool recently. In this paper, we develop a new, heuristic, graph-structure...
In this paper we introduce an information theoretic approach and use techniques from the theory of Huffman codes to construct a sequence of binary sampling vectors to determine a s...
In this paper, we develop algorithms for robust linear regression by leveraging the connection between the problems of robust regression and sparse signal recovery. We explicitly ...
Abstract—The Dantzig selector is a recently introduced technique for near-optimal estimatation of sparse signals from a limited set of measurements. This paper offers an interpre...
This paper studies the feasibility and investigates various choices in the application of compressive sensing (CS) to object-based surveillance video coding. The residual object e...