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| Record Number | 10257 |
| Reference Type | Journal Article |
| Author(s) | Ting, J.;D'Souza, A.;Schaal, S. |
| Year | in press |
| Title | Efficient learning and feature detection in high dimensional spaces |
| Journal/Conference/Book Title | Neural Computation |
| Keywords | high-dimensional regression, feature selection, generalized linear models, variational bayesian methods, sparse bayesian learning |
Abstract |
We present a novel algorithm for efficient learning and feature selection in high-
dimensional regression problems. We arrive at this model through a modification of
the standard regression model, enabling us to derive a probabilistic version of the
well-known statistical regression technique of backfitting. Using the Expectation-
Maximization algorithm, along with variational approximation methods to overcome
intractability, we extend our algorithm to include automatic relevance detection
of the input features. This Variational Bayesian Least Squares (VBLS) approach
retains its simplicity as a linear model, but offers a novel statistically robust “black-
box” approach to generalized linear regression with high-dimensional inputs. It can
be easily extended to nonlinear regression and classification problems. In particular,
we derive the framework of sparse Bayesian learning, e.g., the Relevance Vector
Machine, with VBLS at its core, offering significant computational and robustness
advantages for this class of methods. We evaluate our algorithm on synthetic and
neurophysiological data sets, as well as on standard regression and classification
benchmark data sets, comparing it with other competitive statistical approaches
and demonstrating its suitability as a drop-in replacement for other generalized
linear regression techniques.
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| Notes | clmc |
| URL(s) | http://www-clmc.usc.edu/publications/ting-NC2009.pdf
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| Short Title | Efficient learning and feature detection in high dimensional spaces |
| Papers are available as Adobe PDF ".pdf" files. Adobe Reader is available for free for all computer platforms.
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Page last modified on August 10, 2006, at 06:47 PM
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