Yaghoobi, M., Blumensath, T. and Davies, M.
Regularized dictionary learning for sparse approximation
At 16th Annual European Signal Processing Conference (EUSIPCO), Switzerland.
25 - 29 Aug 2008.
Full text not available from this repository.
Sparse signal models approximate signals using a small number of
elements from a large set of vectors, called a dictionary. The success
of such methods relies on the dictionary fitting the signal structure.
Therefore, the dictionary has to be designed to fit the signal
class of interest. This paper uses a general formulation that allows
the dictionary to be learned form the data with some a priori information
about the dictionary. In this formulation a universal cost
function is proposed and practical algorithms are presented to minimize
this cost under different constraints on the dictionary. The
proposed methods are compared with previous approaches using
synthetic and real data. Simulations highlight the advantages of the
proposed methods over other currently available dictionary learning
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