A unifying framework for vector-valued manifold regularization and multi-view learning


Minh Hà Quang, Loris Bazzani, Vittorio Murino ;
Proceedings of the 30th International Conference on Machine Learning, PMLR 28(2):100-108, 2013.


This paper presents a general vector-valued reproducing kernel Hilbert spaces (RKHS) formulation for the problem of learning an unknown functional dependency between a structured input space and a structured output space, in the Semi-Supervised Learning setting. Our formulation includes as special cases Vector-valued Manifold Regularization and Multi-view Learning, thus provides in particular a unifying framework linking these two important learning approaches. In the case of least square loss function, we provide a closed form solution with an efficient implementation. Numerical experiments on challenging multi-class categorization problems show that our multi-view learning formulation achieves results which are comparable with state of the art and are significantly better than single-view learning.

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