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Dictionary learning with equiprobable matching pursuit
2017
2017 International Joint Conference on Neural Networks (IJCNN)
Sparse signal representations based on linear combinations of learned atoms have been used to obtain state-ofthe-art results in several practical signal processing applications. Approximation methods are needed to process high-dimensional signals in this way because the problem to calculate optimal atoms for sparse coding is NP-hard. Here we study greedy algorithms for unsupervised learning of dictionaries of shiftinvariant atoms and propose a new method where each atom is selected with the
doi:10.1109/ijcnn.2017.7965902
dblp:conf/ijcnn/SandinC17
fatcat:2lo7oi3se5byxioielwlrvqw2y