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ICASSP
2010
IEEE

An L1 criterion for dictionary learning by subspace identification

13 years 11 months ago
An L1 criterion for dictionary learning by subspace identification
We propose an ℓ1 criterion for dictionary learning for sparse signal representation. Instead of directly searching for the dictionary vectors, our dictionary learning approach identifies vectors that are orthogonal to the subspaces in which the training data concentrate. We study conditions on the coefficients of training data that guarantee that ideal normal vectors deduced from the dictionary are local optima of the criterion. We illustrate the behavior of the criterion on a 2D example, showing that the local minima correspond to ideal normal vectors when the number of training data is sufficient. We conclude by describing an algorithm that can be used to optimize the criterion in higher dimension.
Florent Jaillet, Rémi Gribonval, Mark D. Pl
Added 06 Dec 2010
Updated 06 Dec 2010
Type Conference
Year 2010
Where ICASSP
Authors Florent Jaillet, Rémi Gribonval, Mark D. Plumbley, Hadi Zayyani
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