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Compressed Sensing by Gitta Kutyniok, Yonina C. Eldar

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6   Adaptive sensing for sparse recovery

Jarvis Haupt and Robert Nowak

In recent years, tremendous progress has been made in high-dimensional inference problems by exploiting intrinsic low-dimensional structure. Sparsity is perhaps the simplest model for low-dimensional structure. It is based on the assumption that the object of interest can be represented as a linear combination of a small number of elementary functions, which are assumed to belong to a larger collection, or dictionary, of possible functions. Sparse recovery is the problem of determining which components are needed in the representation based on measurements of the object. Most theory and methods for sparse recovery are based on an assumption of non-adaptive measurements. This ...

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