Regularized sparse feature selection with constraints embedded in graph Laplacian matrix

Abstract

Feature selection is an important pre-processing stage in many machine learning and pattern recognition tasks, which eliminates irrelevant and redundant features and improves learning performance. Regularized sparse feature selection methods like Lasso and its variants using ℓ1-norm regularization term in their optimization problem have received much… (More)

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