Robust GEPSVM classifier: An efficient iterative optimization framework.

• An iterative optimization framework is proposed to control the effect of outliers. • An iterative algorithm is designed to solve the general L p -norm optimization problem. • The iterative algorithm converges to a local optimum by rigorous theoretical analysis. • We can adjust the parameters of the GEPSVM Lp to balance the accuracy and training time. The proximal support vector machine via generalized eigenvalues (GEPSVM) is a well-known pattern classification method. GEPSVM, however, is prone to outliers due to its use of the squared L 2 -norm distance criterion. A robust GEPSVM version is proposed to tackle this problem using L 1 -norm distance optimization technique (GEPSVM L1). As optimizing a GEPSVM L1 with L 1 -norm terms can be challenging, we have developed an iterative algorithm to address the L 1 -norm ratio problem associated with GEPSVM L1. Furthermore, an efficient iterative optimization framework has been developed to conveniently address related optimization problems. The research contribution of this paper lies in providing a theoretical analysis of the algorithm's convergence. Besides, we find that the L 1 -norm distance-based methods in real-world applications, especially for handling samples with outliers, sometimes provides an unsatisfactory recognition result. Thus, a generalized version of GEPSVM L1 is proposed. The L 1 -norm distance is replaced with a L p -norm distance in GEPSVM L1 (GEPSVM Lp). It is the robust counterpart of GEPSVM L1 and GEPSVM. It is worth noting that we fine-tune GEPSVM Lp 's parameters to strike a balance between training time and classification accuracy, an especially crucial step for larger datasets. Our experiments indicate that the proposed GEPSVM Lp is more efficient and robust than the competitors in numerous experimental settings. Overall, our work demonstrates the importance of developing robust pattern classification methods in the presence of outliers and provides a practical solution for handling such cases in real-world applications. [ABSTRACT FROM AUTHOR]