Feature Selection Through Lasso

Google Tech Talks November 21, 2006 ABSTRACT Information technology advances are making data collection possible in most if not all fields of science and engineering and beyond. Statistics as a scientific discipline is challenged and enriched by the new opportunities resulted from these high-dimensional data sets. Often data reduction or feature selection is the first step towards solving these massive data problems. However, data reduction through model selection or l_0 constrained least squares optimization leads to a combinatorial search which is computationally infeasible for massive data problems. A computationally efficient alternative is the l_1 constrained least squares optimization or...