Laplacian Generalized Eigenvalues Extreme Learning Machine

摘要

Semi-supervised learning is an attractive technique for using unlabeled data in classification. In this work, an efficient semi-supervised extreme learning machine (ELM) classification framework is proposed by introducing the Laplacian regularization term. It tries to build two nonparallel hyperplanes such that each hyperplane is closer to one of the two classes and farther away from the other class in ELM feature space, based on which, two new semi-supervised ELM classification algorithms are proposed. First, we formulate semi-supervised ELM as a ratio form, and reformulate it as a generalized eigenvalue problem (called Laplacian generalized eigenvalue extreme learning machine, LapGELM) to solve simply. Then we replace the form of ratio with a form of difference so that the optimization problems can be transformed into a standard eigenvalue problem (called Laplacian standard eigenvalue ELM, LapSELM) to control overfit and solve quickly. Furthermore, the proposed algorithms are implemented on various datasets with different sizes and structures. Compared with traditional methods, experiment results show the feasibility and effectiveness of the proposed algorithms due to their simplicity, rapidity, and good generalization performance.