PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Data dependent kernels in nearly-linear time
Guy Lever, Tom Diethe and John Shawe-Taylor
In: AIstats 2012, 21 April 2012, La Palma, Canary Islands.

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.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:9238
Deposited By:Guy Lever
Deposited On:21 February 2012