• DOI:10.1109/LSP.2014.2345761
Corpus ID: 13342762

On the Computational Intractability of Exact and Approximate Dictionary Learning

@article{Tillmann2015OnTC,
  title={On the Computational Intractability of Exact and Approximate Dictionary Learning},
  author={A. Tillmann},
  journal={IEEE Signal Processing Letters},
  year={2015},
  volume={22},
  pages={45-49}
}

A. Tillmann

Published 2015

Computer Science, Mathematics

IEEE Signal Processing Letters

The efficient sparse coding and reconstruction of signal vectors via linear observations has received a tremendous amount of attention over the last decade. In this context, the automated learning of a suitable basis or overcomplete dictionary from training data sets of certain signal classes for use in sparse representations has turned out to be of particular importance regarding practical signal processing applications. Most popular dictionary learning algorithms involve NP-hard sparse… CONTINUE READING

View on IEEE

arxiv.org

Save to Library

Create Alert

Cite

Launch Research Feed