@incollection{eprints1528,
           pages = {1--5},
       publisher = {IEEE},
           title = {Fast design of efficient dictionaries for sparse representations},
       booktitle = {IEEE International Workshop on Machine Learning for Signal Processing (MLSP), 2012},
          author = {Cristian Rusu},
            year = {2012},
        keywords = {K-SVD algorithm, T-mindot, clustering, large datasets training, representation errors, signal processing, sparse representations },
             url = {http://eprints.imtlucca.it/1528/},
        abstract = {One of the central issues in the field of sparse representations is the design of overcomplete dictionaries with a fixed sparsity level from a given dataset. This article describes a fast and efficient procedure for the design of such dictionaries. The method implements the following ideas: a reduction technique is applied to the initial dataset to speed up the upcoming procedure; the actual training procedure runs a more sophisticated iterative expanding procedure based on K-SVD steps. Numerical experiments on image data show the effectiveness of the proposed design strategy.}
}