Supervised classification is one of the most used methods in machine learning. In case of data characterized by a large number of features, a critical issue is to deal with redundant or irrelevant information. In order to get and effective classifier it is necessary to identify a set of features, as small as possible, able to determine the discrimination. Regec-L1 is a classifier with embedded feature selection, based on the Regularized General Eigenvalue Classifier (ReGEC), equipped with a L1-norm regularization term. This approach is able to produce a remakable selection of the features, without losing accuracy in the classification. In that respect, our algorithm seems to compare favorably with the SVM\_L1 method.

M. Viola, M. Sangiovanni, G. Toraldo, and Mario R. Guarracino A Generalized Eigenvalues Classifier with Embedded Feature Selection Optimization Letters, 2015.

DOI: 10.1007/s11590-015-0955-7

Mara Sangiovanni was supported by Interomics Italian Flagship Project and MIUR PON02-00612. Mario Guarracino and Gerardo Toraldo were partially supported by INdAM-GNCS, under the 2015 Project "Numerical Methods for Nonconvex/Nonsmooth Optimization and Applications". Mario Guarracino work has been conducted at National Research University Higher School of Economics (HSE) and has been supported by the RSF grant n. 14-41-00039. Marco Viola work was performed during his undergraduate stage at the Institute for High Performance Computing and Networking (ICAR) of the National Research Council (CNR).