Abstract
In this paper, we propose a novel manifold regularized online semi-supervised learning (OS\(^2\)L) model in an Reproducing Kernel Hilbert Space (RK-HS). The proposed algorithm, named Model-Based Online Manifold Regularization (MOMR), is derived by solving a constrained optimization problem, which is different from the stochastic gradient algorithm used for solving the online version of the primal problem of Laplacian support vector machine (LapSVM). Taking advantage of the convex property of the proposed model, an exact solution can be obtained iteratively by solving its Lagrange dual problem. Furthermore, a buffering strategy is introduced to improve the computational efficiency of the algorithm. Finally, the proposed algorithm is experimentally shown to have a comparable performance to the standard batch manifold regularization algorithm.
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Acknowledgment
This work is partly supported by NFSC grants 61375005 and MOST grants 2015BAK-35B01.
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Ding, S., Xi, X., Liu, Z., Qiao, H., Zhang, B. (2016). A Novel Manifold Regularized Online Semi-supervised Learning Algorithm. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9947. Springer, Cham. https://doi.org/10.1007/978-3-319-46687-3_66
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