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Least squares twin support vector machines are a new non-parallel hyperplane classifier, in which the primal optimization problems of twin support vector machines are modified in least square sense and inequality constraints are replaced by equality constraints. In classification problems, enhancing the robustness of least squares twin support vector machines and reducing the time complexity of kernel function evaluation of a new example when inferring the label of a new example are very important. In this paper, we propose a new sparse least squares twin support vector machines based on manifold-preserving graph reduction which is an efficient graph reduction algorithm with manifold assumption. This method first selects informative examples for positive examples and negative examples, respectively and then applies them for classification. Experimental results confirm the feasibility and effectiveness of our proposed method.

Sparse Least Squares Twin Support Vector Machines with Manifold-preserving Graph Reduction