An Advanced Least Squares Twin Multi-Class Classification Support Vector Machine For Few-Shot Classification

In classification tasks, deep learning methods yield high performance. However, owing to lack of enough annotated data, deep learning methods often underperformed. Therefore, we propose an advance version of least squares twin multi-class classification support vector machine (ALST-KSVC) which leads to low computational complexity and comparable accuracy based on LST-KSVC for few-shot classification. In ALST-KSVC, we modified optimization problems to construct a new "1-versus-1-versus-1" structure, proposed a new decision function, and constructed smaller number of classifiers than our baseline LST-KSVC. We empirically demonstrate that the proposed method has better classification accuracy than LST-KSVC. Especially, ALST-KSVC achieves the state-of-the-art performance on MNIST, USPS, Amazon, Caltech image datasets and Iris, Teaching evaluation, Balance, Wine, Transfusion UCI datasets.