Safe Feature Elimination for the LASSO and Sparse Supervised Learning Problems

We describe a fast method to eliminate features (variables) in l1 -penalized least-square regression (or LASSO) problems. The elimination of features leads to a potentially substantial reduction in running time, specially for large values of the penalty parameter. Our method is not heuristic: it onl...

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Veröffentlicht in:	arXiv.org 2011-05
Hauptverfasser:	Laurent El Ghaoui, Viallon, Vivian, Rabbani, Tarek
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Heuristic methods Parallel processing Run time (computers) Supervised learning Support vector machines
Online-Zugang:	Volltext
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