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  4. MoTIF: An Efficient Algorithm for Learning Translation Invariant Dictionaries
 
conference paper

MoTIF: An Efficient Algorithm for Learning Translation Invariant Dictionaries

Jost, P.  
•
Lesage, S.
•
Vandergheynst, P.  
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2006
2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings

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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