A novel dimensionality reduction method for pattern classification

In this paper, we propose a new algorithm for classification of multi-dimensional data, in which noisy features are distributed in different dimensions of different groups. This kind of datasets violate the assumption of many existing dimension reduction methods, which assume all the groups have the noisy features in the same dimensions and the pruning operation is conducted on the same dimensions of all the groups. Our strategy to resolve this problem is to use multi-classifiers. Each classifier engages different set of dimensions and carries out dimensionality reduction separately. Experiment results on six real world data sets show that the proposed algorithm has a superior to existing ones.