Joint noise detection and L_2,p-norm metric in least squares twin SVM for robust multiclass classification

The least squares twin support vector machine (LSTSVM) serves as a foundational framework for binary classification and is widely applied in statistical learning due to its solid theoretical foundation. It also plays a crucial role in advancing research in multiclass classification. However, the presence of noise in real-world datasets often leads to substantial performance degradation, compromising the reliability and generalizability of this model. Given the ubiquitous presence of noise, its influence on the learning of classification hyperplanes warrants rigorous attention. In this paper, we propose a robust multiclass classification model grounded in LSTSVM, designed to mitigate the influence of noisy data. The proposed framework replaces the conventional squared L₂-norm with the more robust L_2,p-norm (0