A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is
2010 Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers
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. 978-1-4244-9721-8/10/$26.00doi:10.1109/acssc.2010.5757904 fatcat:xlojbm3fxng73fzjnxztgisfga