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NIPS
2003

Kernel Dimensionality Reduction for Supervised Learning

11 years 6 months ago
Kernel Dimensionality Reduction for Supervised Learning
We propose a novel method of dimensionality reduction for supervised learning. Given a regression or classification problem in which we wish to predict a variable Y from an explanatory vector X, we treat the problem of dimensionality reduction as that of finding a low-dimensional “effective subspace” of X which retains the statistical relationship between X and Y . We show that this problem can be formulated in terms of conditional independence. To turn this formulation into an optimization problem, we characterize the notion of conditional independence using covariance operators on reproducing kernel Hilbert spaces; this allows us to derive a contrast function for estimation of the effective subspace. Unlike many conventional methods, the proposed method requires neither assumptions on the marginal distribution of X, nor a parametric model of the conditional distribution of Y .
Kenji Fukumizu, Francis R. Bach, Michael I. Jordan
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2003
Where NIPS
Authors Kenji Fukumizu, Francis R. Bach, Michael I. Jordan
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