Data dependent kernels in nearly-linear time

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Guy Lever, Tom Diethe, John Shawe-Taylor ;
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, PMLR 22:685-693, 2012.

Abstract

We propose a method to efficiently construct data dependent kernels which can make use of large quantities of (unlabeled) data. Our construction makes an approximation in the standard construction of semi-supervised kernels in Sindhwani et al. (2005). In typical cases these kernels can be computed in nearly-linear time (in the amount of data), improving on the cubic time of the standard construction, enabling large scale semi-supervised learning in a variety of contexts. The methods are validated on semi-supervised and unsupervised problems on data sets containing upto 64,000 sample points.

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