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Feature Selection via Detecting Ineffective Features

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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