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An Optimal Basis for Feature Extraction With Support Vector Machine Classification Using The Radius-Margin Bound | IEEE Conference Publication | IEEE Xplore

An Optimal Basis for Feature Extraction With Support Vector Machine Classification Using The Radius-Margin Bound


Abstract:

A method is presented for deriving an optimal basis for features classified with a support vector machine. The method is based on minimizing the leave-one-out error which...Show More

Abstract:

A method is presented for deriving an optimal basis for features classified with a support vector machine. The method is based on minimizing the leave-one-out error which is approximated by the radius-margin bound. A gradient descent method provides a learning rule for the basis in an outer loop of an iteration. The inner loop performs support vector machine training and provides support vector coefficients on which the gradient descent depends. In this way, the derivation of a basis for feature extraction and the support vector machine are jointly optimized. The efficacy of the method is illustrated with examples from multi-dimensional synthetic data sets
Date of Conference: 14-19 May 2006
Date Added to IEEE Xplore: 24 July 2006
Print ISBN:1-4244-0469-X

ISSN Information:

Conference Location: Toulouse, France

1. INTRODUCTION

When data from multi-dimensional measurements is represented as a feature vector, the feature space of the raw data often has a very large dimension. It is usually prudent to re-represent the original measurements in more compact form (a shorter feature vector). The process of selecting features from the raw measurements is one of feature selection or feature extraction. It is, in general, a problem of dimensionality reduction. The ultimate goal of feature selection/extraction is to find the minimum number of features required to capture the essential structure in the raw data. This minimum number of features is termed the intrinsic dimensionality of the data. This dimensionality reduction is accomplished by applying a transformation (linear or non-linear) to the the input data.

References

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