Nonparametric prior for adaptive sparsity

Vikas Raykar, Linda Zhao
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:629-636, 2010.

Abstract

For high-dimensional problems various parametric priors have been proposed to promote sparse solutions. While parametric priors has shown considerable success they are not very robust in adapting to varying degrees of sparsity. In this work we propose a discrete mixture prior which is partially nonparametric. The right structure for the prior and the amount of sparsity is estimated directly from the data. Our experiments show that the proposed prior adapts to sparsity much better than its parametric counterparts. We apply the proposed method to classification of high dimensional microarray datasets.

Cite this Paper

BibTeX
@InProceedings{pmlr-v9-raykar10a, title = {Nonparametric prior for adaptive sparsity}, author = {Raykar, Vikas and Zhao, Linda}, booktitle = {Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics}, pages = {629--636}, year = {2010}, editor = {Teh, Yee Whye and Titterington, Mike}, volume = {9}, series = {Proceedings of Machine Learning Research}, address = {Chia Laguna Resort, Sardinia, Italy}, month = {13--15 May}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v9/raykar10a/raykar10a.pdf}, url = {https://proceedings.mlr.press/v9/raykar10a.html}, abstract = {For high-dimensional problems various parametric priors have been proposed to promote sparse solutions. While parametric priors has shown considerable success they are not very robust in adapting to varying degrees of sparsity. In this work we propose a discrete mixture prior which is partially nonparametric. The right structure for the prior and the amount of sparsity is estimated directly from the data. Our experiments show that the proposed prior adapts to sparsity much better than its parametric counterparts. We apply the proposed method to classification of high dimensional microarray datasets.} }
Endnote
%0 Conference Paper %T Nonparametric prior for adaptive sparsity %A Vikas Raykar %A Linda Zhao %B Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2010 %E Yee Whye Teh %E Mike Titterington %F pmlr-v9-raykar10a %I PMLR %P 629--636 %U https://proceedings.mlr.press/v9/raykar10a.html %V 9 %X For high-dimensional problems various parametric priors have been proposed to promote sparse solutions. While parametric priors has shown considerable success they are not very robust in adapting to varying degrees of sparsity. In this work we propose a discrete mixture prior which is partially nonparametric. The right structure for the prior and the amount of sparsity is estimated directly from the data. Our experiments show that the proposed prior adapts to sparsity much better than its parametric counterparts. We apply the proposed method to classification of high dimensional microarray datasets.
RIS
TY - CPAPER TI - Nonparametric prior for adaptive sparsity AU - Vikas Raykar AU - Linda Zhao BT - Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics DA - 2010/03/31 ED - Yee Whye Teh ED - Mike Titterington ID - pmlr-v9-raykar10a PB - PMLR DP - Proceedings of Machine Learning Research VL - 9 SP - 629 EP - 636 L1 - http://proceedings.mlr.press/v9/raykar10a/raykar10a.pdf UR - https://proceedings.mlr.press/v9/raykar10a.html AB - For high-dimensional problems various parametric priors have been proposed to promote sparse solutions. While parametric priors has shown considerable success they are not very robust in adapting to varying degrees of sparsity. In this work we propose a discrete mixture prior which is partially nonparametric. The right structure for the prior and the amount of sparsity is estimated directly from the data. Our experiments show that the proposed prior adapts to sparsity much better than its parametric counterparts. We apply the proposed method to classification of high dimensional microarray datasets. ER -
APA
Raykar, V. & Zhao, L.. (2010). Nonparametric prior for adaptive sparsity. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 9:629-636 Available from https://proceedings.mlr.press/v9/raykar10a.html.

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