Representing transformation invariances in data is known to be valuable in many domains. We consider a method by which prior knowledge about the structure of such invariances can be exploited using a novel algorithm for sparse coding across a learned dictionary of atoms combined with a parameterized deformation function that captures invariant structure. We demonstrate the value of this on both reconstructing signals, as well as improved unsupervised grouping based on invariant sparse representations.
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