Multi-view semi-supervised least squares twin support vector machines with manifold-preserving graph reduction

Multi-view semi-supervised support vector machines consider learning with multi-view unlabeled data to boost the learning performance. However, they have several defects. They need to solve the quadratic programming problem and the time complexity is quite high. Moreover, when a large number of multi-view unlabeled examples exist, it can generate more outliers and noisy examples and influence the performance. Therefore, in this paper, we propose two novel multi-view semi-supervised support vector machines called multi-view Laplacian least squares twin support vector machine and its improved version with the manifold-preserving graph reduction which can enhance the robustness of the algorithm. They can reduce the time complexity by changing the constraints to a series of equality constraints and lead to a pair of linear equations. The linear multi-view Laplacian least squares twin support vector machine and its improved version with manifold-preserving graph reduction are further generalized to the nonlinear case via the kernel trick. Experimental results demonstrate that our proposed methods are effective.