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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 - #308259 |
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Title:
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Dimension Augmenting Vector Machine: A New General Classifier System for Large p Small n Problem
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Author(s):
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Samiran Ghosh*+ and Yazhen Wang and Dipak Dey
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Companies:
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Indiana University Purdue University Indianapolis and University of Connecticut and University of Connecticut
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Address:
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402 N Blackford Street LD270, Indianapolis, IN, 46202,
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Keywords:
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Classification ; Import Vector Machine ; Radial Basis Function ; Regularization ; Reproducing Kernel Hilbert Space ; Support Vector Machine
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Abstract:
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Support vector machine and other reproducing kernel Hilbert space based classifier systems are drawing much attention recently due to its robustness and generalization capability. All of these approaches construct classifier based on training sample in a high dimensional space by using all available dimensions. SVM achieves huge data compression by selecting only few observations lying in the boundary of the classifier function. However when the number of observations are small but the number of dimensions are very large then it is not necessary that all available dimensions are carrying equal information in the classification context. Selection of only useful fraction of available dimensions will result in huge data compression. In this paper, we have come up with an approach parallel to IVM introduced by Zhu et al., by means of which such an optimal set of dimension could be selected.
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