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ICML
2008
IEEE

Sparse multiscale gaussian process regression

15 years 1 months ago
Sparse multiscale gaussian process regression
Most existing sparse Gaussian process (g.p.) models seek computational advantages by basing their computations on a set of m basis functions that are the covariance function of the g.p. with one of its two inputs fixed. We generalise this for the case of Gaussian covariance function, by basing our computations on m Gaussian basis functions with arbitrary diagonal covariance matrices (or length scales). For a fixed number of basis functions and any given criteria, this additional flexibility permits approximations no worse and typically better than was previously possible. We perform gradient based optimisation of the marginal likelihood, which costs O(m2 n) time where n is the number of data points, and compare the method to various other sparse g.p. methods. Although we focus on g.p. regression, the central idea is applicable to all kernel based algorithms, and we also provide some results for the support vector machine (s.v.m.) and kernel ridge regression (k.r.r.). Our approach outp...
Bernhard Schölkopf, Christian Walder, Kwang I
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2008
Where ICML
Authors Bernhard Schölkopf, Christian Walder, Kwang In Kim
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