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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Hauptverfasser:	Ghaoui, Laurent El, Viallon, Vivian, Rabbani, Tarek
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Learning Computer Science - Systems and Control Mathematics - Optimization and Control
Online-Zugang:	Volltext bestellen
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