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Activity Number:
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189
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Type:
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Topic Contributed
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Date/Time:
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Monday, July 30, 2007 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #309699 |
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Title:
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Learning Gradients and Feature Selection on Manifolds
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Author(s):
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Sayan Mukherjee*+
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Companies:
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Duke University
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Address:
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101 Science Drive, Durham, NC, 27708,
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Keywords:
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Manifold learning ; Dimensionality reduction and regression ; Kernel models
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Abstract:
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An underlying premise in the analysis and modeling of high-dimensional physical and biological systems is that data generated by measuring thousands of variables lie on or near a low-dimensional manifold. This premise has led to various estimation and learning problems grouped under the heading of ``manifold learning.'' It is natural to formulate the problem of feature selection---finding salient variables (or linear combinations of salient variables) and estimating how they covary---in the manifold setting. For regression and classification the idea of selecting features via estimates of the gradient of the regression and classification function has been developed. In this paper we extend this approach from the Euclidean setting to the manifold setting.
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