Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
530738 | Pattern Recognition | 2008 | 12 Pages |
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.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Guoping Qiu, Jianzhong Fang,