Robust L2, 1-Norm Distance Enhanced Multi-Wight Vector Projection Support Vector Machine

The enhanced multi-weight vector projection support vector machine (EMVSVM) is an outstanding algorithm for binary classification, which is proposed recently. However, it measures the distances in an objective function by the squared L-2-norm, which exaggerates the effects of outliers or noisy data. In order to alleviate this problem, we propose an effective novel EMVSVM, termed robust EMVSVM based on the L-2(, 1)-norm distance (L-2(, 1)-EMVSVM). The distances in the objective of our algorithm are measured by the L-2(, 1)-norm. Besides, a new powerful iterative algorithm is designed to solve the formulated objective, whose convergence is ensured by theoretical proofs. Finally, the effectiveness and robustness of L-2(, 1)-EMVSVM are verified through extensive experiments.