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.

多视图半监督支持向量机考虑使用多视图未标记数据进行学习以提高学习性能。但是，它们有几个缺陷。他们需要解决二次编程问题，并且时间复杂度很高。此外，当存在大量的多视图未标记示例时，它会生成更多的异常值和嘈杂的示例并影响性能。因此，在本文中，我们提出了两种新颖的多视图半监督支持向量机，称为多视图Laplacian最小二乘孪生支持向量机及其改进的版本，具有流形保留图约简，可以增强算法的鲁棒性。它们可以通过将约束更改为一系列等式约束来减少时间复杂度，并生成一对线性方程。线性多视图拉普拉斯最小二乘双支持向量机及其带有流形保留图约简的改进版本通过核技巧进一步推广到非线性情况。实验结果表明，我们提出的方法是有效的。