| Paper: | MLSP-P5.8 | ||
| Session: | Blind Source Separation III | ||
| Time: | Friday, May 19, 14:00 - 16:00 | ||
| Presentation: | Poster | ||
| Topic: | Machine Learning for Signal Processing: Blind Signal Separation and Independent Component Analysis | ||
| Title: | MoTIF : an Efficient Algorithm for Learning Translation Invariant Dictionaries | ||
| Authors: | Philippe Jost, Pierre Vandergheynst, Swiss Federal Institute of Technology Lausanne (EPFL), Switzerland; Sylvain Lesage, Rémi Gribonval, Institut de Recherche en Informatique et Systèmes Aléatoires, France | ||
| 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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