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Author: Niharika Gauraha

Affiliation: Indian Statistical Institute, India

ISBN: 978-989-758-173-1

Keyword(s): Lasso, Stability Selection, Cluster Representative Lasso, Cluster Group Lasso.

Related Ontology Subjects/Areas/Topics: Feature Selection and Extraction ; Pattern Recognition ; Regression ; Sparsity ; Theory and Methods

Abstract: Variable selection in high dimensional regression problems with strongly correlated variables or with near linear dependence among few variables remains one of the most important issues. We propose to cluster the variables first and then do stability feature selection using Lasso for cluster representatives. The first step involves generation of groups based on some criterion and the second step mainly performs group selection with controlling the number of false positives. Thus, our primary emphasis is on controlling type-I error for group variable selection in high-dimensional regression setting. We illustrate the method using simulated and pseudo-real data, and we show that the proposed method finds an optimal and consistent solution.

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Paper citation in several formats:
Gauraha, N. (2016). Stability Feature Selection using Cluster Representative LASSO.In Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-173-1, pages 381-386. DOI: 10.5220/0005827003810386

@conference{icpram16,
author={Niharika Gauraha.},
title={Stability Feature Selection using Cluster Representative LASSO},
booktitle={Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2016},
pages={381-386},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005827003810386},
isbn={978-989-758-173-1},
}

TY - CONF

JO - Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Stability Feature Selection using Cluster Representative LASSO
SN - 978-989-758-173-1
AU - Gauraha, N.
PY - 2016
SP - 381
EP - 386
DO - 10.5220/0005827003810386

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