NPSVC++: Nonparallel Classifiers Encounter Representation Learning
This work solves the bottleneck of integrating representation learning into NPSVCs for researchers in machine learning, though it is incremental as it builds on existing NPSVC frameworks.
The paper tackles the problem of improving nonparallel support vector classifiers (NPSVCs) by addressing feature suboptimality and class dependency through representation learning, resulting in NPSVC++ which demonstrates superiority over existing methods in experiments.
This paper focuses on a specific family of classifiers called nonparallel support vector classifiers (NPSVCs). Different from typical classifiers, the training of an NPSVC involves the minimization of multiple objectives, resulting in the potential concerns of feature suboptimality and class dependency. Consequently, no effective learning scheme has been established to improve NPSVCs' performance through representation learning, especially deep learning. To break this bottleneck, we develop NPSVC++ based on multi-objective optimization, enabling the end-to-end learning of NPSVC and its features. By pursuing Pareto optimality, NPSVC++ theoretically ensures feature optimality across classes, hence effectively overcoming the two issues above. A general learning procedure via duality optimization is proposed, based on which we provide two applicable instances, K-NPSVC++ and D-NPSVC++. The experiments show their superiority over the existing methods and verify the efficacy of NPSVC++.