In a Sparse Representation a vector x is represented or approximated as a linearĬombination of some few of the dictionary atoms. The dictionary is usually used for Sparse Representation or Approximation of signals.Ī dictionary is a collection of atoms, here the atoms are real column vectors of length N.Ī finite dictionary of K atoms can be represented I highly recommend Elad's (2010) book: "Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing"ĭictionary Learning is a topic in the Signal Processing area,.Michael Elad has done much research on Sparse Representations and Dictionary Learning,.You may also see Skretting's PhD thesisįor more on Dictionary (called Frame in the thesis) Learning.The documentation for the Java package with files for Matching Pursuit and Dictionary Learning by Skretting. "Sparse Approximation by Matching Pursuit using Shift-Invariant Dictionary" by Skretting and Engan. "Learned dictionaries for sparse image representation: Properties and results" by Skretting and Engan. "Image compression using learned dictionaries by RLS-DLA and compared with K-SVD" by Skretting and Engan. Paper presented at NORSIG 2003, by Skretting and Husøy. The page for the SPArse Modeling Software by Mairal. The Online Dictionary Learning for Sparse Coding paper by Mairal et al. The Recursive Least Squares Dictionary Learning Algorithm paper by Skretting and Engan. The K-SVD method for dictionary learning by Aharon et al. ILS-DLA includes Method of Optimized Directions (MOD). The Iterative Least Squares Dictionary Learning Algorithm by Engan et al.
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