Stochastic Low-Rank Kernel Learning for Regression

Details Stochastic Low-Rank Kernel Learning for Regression Stochastic Low-Rank Kernel Learning for Regression

2012-01-11

arxiv.org/abs/1201.2416v1

Computer Science

Learning

International Conference on Machine Learning (ICML'11), Bellevue (Washington) : United States (2011)

Scientific paper

We present a novel approach to learn a kernel-based regression function. It is based on the useof conical combinations of data-based parameterized kernels and on a new stochastic convex optimization procedure of which we establish convergence guarantees. The overall learning procedure has the nice properties that a) the learned conical combination is automatically designed to perform the regression task at hand and b) the updates implicated by the optimization procedure are quite inexpensive. In order to shed light on the appositeness of our learning strategy, we present empirical results from experiments conducted on various benchmark datasets.

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