%T Clustering large datasets - bounds and applications with K-SVD
%P 1-10
%V 20
%I Politechnica University of Bucharest
%A Cristian Rusu
%K sparse representations, clustering, KSVD. MSC2000: 94A 12.
%D 2012
%L eprints1525
%X This article presents a clustering method called T-mindot that is used to reduce the dimension of datasets in order to diminish the running time of the training algorithms. The T-mindot method is applied before the K-SVD algorithm in the context of sparse representations for the design of
overcomplete dictionaries. Simulations that run on image data show the efficiency of the proposed method that leads to the substantial reduction of the execution time of K-SVD, while keeping the representation performance of the dictionaries designed using the original dataset.
%J UPB Scientific Bulletin, Series C: Electrical Engineering
%N 10