Multi-view Laplacian Support Vector Machines

@inproceedings{Sun2011MultiviewLS,
  title={Multi-view Laplacian Support Vector Machines},
  author={Shiliang Sun},
  booktitle={ADMA},
  year={2011}
}
We propose a new approach, multi-view Laplacian support vector machines (SVMs), for semi-supervised learning under the multi-view scenario. It integrates manifold regularization and multi-view regularization into the usual formulation of SVMs and is a natural extension of SVMs from supervised learning to multi-view semi-supervised learning. The function optimization problem in a reproducing kernel Hilbert space is converted to an optimization in a finite-dimensional Euclidean space. After… CONTINUE READING

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