Detail publikace

On the Limitation of Convex Optimization for Sparse Signal Segmentation

Originální název

On the Limitation of Convex Optimization for Sparse Signal Segmentation

Anglický název

On the Limitation of Convex Optimization for Sparse Signal Segmentation

Jazyk

en

Originální abstrakt

We show that convex optimization methods have fundamental properties that complicate performing signal segmentation based on sparsity assumptions. We review the recently introduced overcomplete sparse segmentation model, we perform experiments revealing the limits, and we explain this behaviour. We also propose modifications and alternatives.

Anglický abstrakt

We show that convex optimization methods have fundamental properties that complicate performing signal segmentation based on sparsity assumptions. We review the recently introduced overcomplete sparse segmentation model, we perform experiments revealing the limits, and we explain this behaviour. We also propose modifications and alternatives.

Dokumenty

BibTex


@inproceedings{BUT124522,
  author="Pavel {Rajmic} and Michaela {Novosadová}",
  title="On the Limitation of Convex Optimization for Sparse Signal Segmentation",
  annote="We show that convex optimization methods have fundamental properties that complicate performing signal segmentation based on sparsity assumptions. We review the recently introduced overcomplete sparse segmentation model, we perform experiments revealing the limits, and we explain this behaviour. We also propose modifications and alternatives.",
  booktitle="Proceedings of the 39th International Conference on Telecommunications and Signal Processing (TSP) 2016",
  chapter="124522",
  doi="10.1109/TSP.2016.7760941",
  howpublished="electronic, physical medium",
  year="2016",
  month="june",
  pages="550--554",
  type="conference paper"
}