Parametric Dictionary Design for Sparse Coding
Mehrdad Yaghoobi, Laurent Daudet, Mike E. Davies
Abstract
Open-access reader
Mehrdad Yaghoobi, Laurent Daudet, Mike E. Davies
Abstract
Open-access reader
This paper introduces a new dictionary design method for sparse coding of a class of signals. It has been shown that one can sparsely approximate some natural signals using an overcomplete set of parametric functions. A problem in using these parametric dictionaries is how to choose the parameters. In practice, these parameters have been chosen by an expert or through a set of experiments. In the sparse approximation context, it has been shown that an incoherent dictionary is appropriate for the sparse approximation methods. In this paper, we first characterize the dictionary design problem, subject to a constraint on the dictionary. Then we briefly explain that equiangular tight frames have minimum coherence. The complexity of the problem does not allow it to be solved exactly. We introduce a practical method to approximately solve it. Some experiments show the advantages one gets by using these dictionaries.
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This paper introduces a new dictionary design method for sparse coding of a class of signals. It has been shown that one can sparsely approximate some natural signals using an overcomplete set of parametric functions. A problem in using these parametric dictionaries is how to choose the parameters. In practice, these parameters have been chosen by an expert or through a set of experiments. In the sparse approximation context, it has been shown that an incoherent dictionary is appropriate for the sparse approximation methods. In this paper, we first characterize the dictionary design problem, subject to a constraint on the dictionary. Then we briefly explain that equiangular tight frames have minimum coherence. The complexity of the problem does not allow it to be solved exactly. We introduce a practical method to approximately solve it. Some experiments show the advantages one gets by using these dictionaries.
Key concepts: K-SVD, Computer science, Parametric statistics, Neural coding, Sparse approximation, Algorithm, Set (abstract data type), Coherence (philosophical gambling strategy)