L21 Regularized Correntropy for Robust Feature Selectionhttp://mloss.orgUpdates and additions to L21 Regularized Correntropy for Robust Feature SelectionenTue, 26 Jun 2012 03:11:55 -0000L21 Regularized Correntropy for Robust Feature Selection 1.0<html><p>We study the problem of robust feature extraction based on L21 regularized correntropy in both theoretical and algorithmic manner. In theoretical part, we point out that an L21-norm minimization can be justified from the viewpoint of half-quadratic (HQ) optimization, which facilitates convergence study and algorithmic development. In particular, a general formulation is accordingly proposed to unify L1-norm and L21-norm minimization within a common framework. In algorithmic part, we propose an L21 regularized correntropy algorithm to extract informative features meanwhile to remove outliers from training data. A new alternate minimization algorithm is also developed to optimize the non-convex correntropy objective. In terms of face recognition, we apply the proposed method to obtain an appearance-based model, called Sparse-Fisherfaces. Extensive experiments show that our method can select robust and sparse features, and outperforms several state-of-the-art subspace methods on largescale and open face recognition datasets. </p></html>ran heTue, 26 Jun 2012 03:11:55 -0000 selectionhql1 norml21 normrobust