Stability feature selection using cluster representative Lasso
Document Type
Conference Article
Publication Title
ICPRAM 2016 - Proceedings of the 5th International Conference on Pattern Recognition Applications 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.
First Page
381
Last Page
386
DOI
10.5220/0005827003810386
Publication Date
1-1-2016
Recommended Citation
Gauraha, Niharika, "Stability feature selection using cluster representative Lasso" (2016). Conference Articles. 804.
https://digitalcommons.isical.ac.in/conf-articles/804
