Classification in an informative sample subspace

Authors:
Guoping Qiu;Jianzhong Fang
Affiliations:
School of Computer Science, University of Nottingham, Jubilee Campus, Nottingham, NG8 1BB, UK and Department of Computer Science, Hong Kong Baptist University, Hong Kong;School of Computer Science, University of Nottingham, Jubilee Campus, Nottingham, NG8 1BB, UK and Endace Technology, Hamilton, New Zealand
Venue:
Pattern Recognition
Year:
2008

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Abstract

We have developed an informative sample subspace (ISS) method that is suitable for projecting high-dimensional data onto a low-dimensional subspace for classification purposes. In this paper, we present an ISS algorithm that uses a maximal mutual information criterion to search a labelled training data set directly for the subspace's projection base vectors. We evaluate the usefulness of the ISS method using synthetic data as well as real world problems. Experimental results demonstrate that the ISS algorithm is effective and can be used as a general method for representing high-dimensional data in a low-dimensional subspace for classification.