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Dictionary learning is usually approached by looking at the support of the sparse representations. Recent years have shown results in dictionary improvement by investigating the cosupport via the analysis-based cosparse model. In this paper we present a new cosparse learning algorithm for orthogonal dictionary blocks that provides significant dictionary recovery improvements and representation error...
This paper treats the problem of learning a dictionary providing sparse representations for a given signal class, via ℓ1-minimization. The problem can also be seen as factorizing a d × N matrix Y = (y1 . . . yN), yn ∈ ℝd of training signals into a d × K dictionary matrix Φ and a K × N coefficient matrix X = (x1 . . . xN), xn ∈ ℝK, which is sparse. The exact question studied here is when a dictionary...
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