Infoscience

Conference paper

MOTIF: AN EFFICIENT ALGORITHM FOR LEARNING TRANSLATION INVARIANT DICTIONARIES

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.

    Keywords: LTS2

    Reference

    • EPFL-CONF-87354

    Record created on 2006-06-14, modified on 2016-08-08

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