MoTIF: An Efficient Algorithm for Learning Translation Invariant Dictionaries
- 1 May 2006
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 5 (15206149)
- https://doi.org/10.1109/icassp.2006.1661411
Abstract
The performance 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 iteratively learning generating functions that can be shifted at all positions in the signal to generate a highly redundant dictionaryKeywords
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