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Feature Selection via Detecting Ineffective Features
This video was recorded at International Workshop on Advances in Regularization, Optimization, Kernel Methods and Support Vector Machines (ROKS): theory and applications, Leuven 2013. Consider the regression problem with a response variable Y and with a feature vector X. For the regression function m(x) = E{Y | X = x}, we introduce a new and simple estimator of the minimum mean squared error L ∗ = E{(Y −m(X))2}. Let X(−k) be the feature vector, in which the k-th component of X is missing. In this paper we analyze a nonparametric test for the hypothesis that the k-th component is ineffective, i.e., E{Y | X} = E{Y | X(−k)} a.s.
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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
