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Deciphering subsampled data: adaptive compressive sampling as a principle of brain communication
[article]
2010
arXiv
pre-print
A new algorithm is proposed for a) unsupervised learning of sparse representations from subsampled measurements and b) estimating the parameters required for linearly reconstructing signals from the sparse codes. We verify that the new algorithm performs efficient data compression on par with the recent method of compressive sampling. Further, we demonstrate that the algorithm performs robustly when stacked in several stages or when applied in undercomplete or overcomplete situations. The new
arXiv:1011.0241v1
fatcat:jkqbu2yg3zduhjegopuq2pdbhe