Polar Operators for Structured Sparse Estimation
Full Text: 4935-polar-operators-for-structured-sparse-estimation.pdf
Structured sparse estimation has become an important technique in many areas of data analysis. Unfortunately, these estimators normally create computational difficulties that entail sophisticated algorithms. Our first contribution is to uncover a rich class of structured sparse regularizers whose polar operator can be evaluated efficiently. With such an operator, a simple conditional gradient method can then be developed that, when combined with smoothing and local optimization, significantly reduces training time vs. the state of the art. We also demonstrate a new reduction of polar to proximal maps that enables more efficient latent fused lasso.
Citation
X. Zhang, Y. Yu, D. Schuurmans. "Polar Operators for Structured Sparse Estimation". Neural Information Processing Systems (NIPS), (ed: C. J. C. Burges, L. Bottou, M. Welling, Z. Ghahramani, K. Q. Weinberger), pp 82-90, December 2013.| Keywords: | |
| Category: | In Conference |
| Web Links: | NeurIPS |
BibTeX
@incollection{Zhang+al:NIPS13,
author = {Xinhua Zhang and Yao-Liang Yu and Dale Schuurmans},
title = {Polar Operators for Structured Sparse Estimation},
Editor = {C. J. C. Burges, L. Bottou, M. Welling, Z. Ghahramani, K. Q.
Weinberger},
Pages = {82-90},
booktitle = {Neural Information Processing Systems (NIPS)},
year = 2013,
}Last Updated: February 19, 2020Submitted by Sabina P
