Most of previous research efforts on support vector machines (SVMs) were directed toward efficient implementations and practical applications. In this work, we concentrate on a different aspect of SVMs. Specifically, we investigate the non-uniqueness of SVM solutions. The key features of this work include (1) we concentrate on the cases where the dual solutions are not unique, whereas the primal solutions are unique; (2) our test for non-uniqueness can be directly applied to data points without solving the SVC optimization problem, namely, the non-uniqueness information is obtained before any numerical optimization procedure is employed.
['Jia-Rui Zhang', 'Shih-Yu Chiu', 'Leu-Shing Lan']
Non-uniqueness of solutions of 1-norm support vector classification in dual form