Abstract / truncated to 115 words (read the full abstract)

The sparse signal recovery, which appears not only in compressed sensing but also in other related problems such as sparse overcomplete representations, denoising, sparse learning, etc. has drawn a large attraction in the last decade. The literature contains a vast number of recovery methods, which have been analysed in theoretical and empirical aspects. This dissertation presents novel search-based sparse signal recovery methods. First, we discuss theoretical analysis of the orthogonal matching pursuit algorithm with more iterations than the number of nonzero elements of the underlying sparse signal. Second, best-fi rst tree search is incorporated for sparse recovery by a novel method, whose tractability follows from the properly de fined cost models and pruning techniques. The ... toggle 5 keywords

compressed sensing – sparse signal recovery – best-first tree search – forward-backward search – mixed integer linear programming.

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