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Tight Measurement Bounds for Exact Recovery of Structured Sparse Signals
Tight Measurement Bounds Exact Recovery Structured Sparse Signals
2011/7/6
Standard compressive sensing results state that to exactly recover an s sparse signal in R^p, one requires O(s\cdotlog p) measurements. While this bound is extremely useful in practice, often real wor...
From Sparse Signals to Sparse Residuals for Robust Sensing
Sparse Signals Sparse Residuals
2010/11/8
One of the key challenges in sensor networks is the extraction of information by fusing data from a multitude of distinct, but possibly unreliable sensors. Recovering information from the maximum numb...
Innovated Higher Criticism for detecting sparse signals in correlated noise
Adding noise Cholesky factorization empirical process innovation multiplehypothesis testing sparse normal means
2010/3/18
Higher Criticism is a method for detecting signals that are both sparse and weak. Although
first proposed in cases where the noise variables are independent, Higher Criticism
also has reasonable per...