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The best algorithm for a computational problem generally depends on the "relevant inputs," a concept that depends on the application domain and often defies formal articulation. While there is a large literature on empirical approaches to selecting the best algorithm for a given application domain, there has been surprisingly little theoretical analysis of(More)
We initiate the study of sparse recovery problems under the Earth-Mover Distance (EMD). Specifically, we design a distribution over m × n matrices A, for m « n, such that for any x, given Ax, we can recover a k-sparse approximation to x under the EMD distance. We also provide an empirical evaluation of the method that show, in some scenarios,(More)
We propose a framework for compressive sensing of images with local geometric features. Specifically, let x &#8712; R<sup>N</sup> be an N-pixel image, where each pixel p has value x<sub>p</sub>. The image is acquired by computing the <i>measurement vector</i> Ax, where A is an m x N measurement matrix for some m l N. The goal is then to design the matrix(More)
We propose a framework for compressive sensing of images with local distinguishable objects, such as stars, and apply it to solve a problem in celestial navigation. Specifically, let x ∈ R N be an N-pixel image, consisting of a small number of local distinguishable objects plus noise. Our goal is to design an m × N measurement matrix A with m N , such that(More)
The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Abstract We propose a framework for compressive sensing of images with local geometric features. Specifically, let x ∈ R N be an N-pixel image, where each pixel p has value x p. The image is acquired by computing the measurement vector Ax,(More)
We propose a framework for compressive sensing of images with local distinguishable objects, such as stars, and apply it to solve a problem in celestial navigation. Specifically, let x E RN be an N-pixel image, consisting of a small number of local distinguishable objects plus noise. Our goal is to design an m x N measurement matrix A with m < N, such that(More)
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