General multi-view learning with maximum entropy discrimination

Abstract

Maximum entropy discrimination (MED) is an excellent discriminative classification algorithm in view of maximum entropy and maximum margin principles, and can generate hard-margin support vector machines (SVMs) under certain condition. Recently, many versions of multi-view maximum entropy discrimination are proved to be effective in multi-view learning. However, those methods are designed only for the two-view classification problem. For example, alternative multi-view maximum entropy discrimination (AMvMED) and multi-kernel multi-view maximum entropy discrimination (MkMvMED) belong to the two-view classification method. In this paper, we extend AMvMED and MkMvMED to the general multi-view classification problems by jointly learning multiple different views in a non-pairwise way called general alternative multi-view maximum entropy discrimination (GAMvMED) and general multi-kernel multi-view maximum entropy discrimination (GM3ED). In GAMvMED, combination weight for the reconstruction of each view in regularization terms is learned to explore complementarity information among different views. Finally, we provide an efficient iteration algorithm with the classical convex quadratic programming for optimization. Simultaneously, we derive that GAMvMED and GM3ED are equivalent in form under the similar prior assumption. Experimental results confirm the effectiveness of our proposed methods.