MoTIF : an Efficient Algorithm for Learning Translation Invariant Dictionaries

Philippe Jost 1 Pierre Vandergheynst 1 Sylvain Lesage 2 Rémi Gribonval 2
2 METISS - Speech and sound data modeling and processing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : The performances of approximation using redundant expansions rely on having dictionaries adapted to the signals. In natural high-dimensional data, the statistical dependencies are, most of the time, not obvious. Learning fundamental patterns is an alternative to analytical design of bases and is nowadays a popular problem in the field of approximation theory. In many situations, the basis elements are shift invariant, thus the learning should try to find the best matching filters. We present a new algorithm for learning iteratively generating functions that can be translated at all positions in the signal to generate a highly redundant dictionary.
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Philippe Jost, Pierre Vandergheynst, Sylvain Lesage, Rémi Gribonval. MoTIF : an Efficient Algorithm for Learning Translation Invariant Dictionaries. Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on, May 2006, Toulouse, France. pp.V -V, ⟨10.1109/ICASSP.2006.1661411⟩. ⟨inria-00544911⟩

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