Nonlinear cluster transformations for increasing pattern separability

The objective of classification is to generate a nonlinear multidimensional decision boundary that partitions the pattern space into prescribed classes. However, these algorithms are successful only when the data is well distributed in their domain. In practice, patterns from different classes can be closely packed with significant overlap. Prior to classification, the data is generally preprocessed so that the intercluster to intracluster distance ratio is maximized. This paper discusses limitations of conventional approaches for preprocessing based on Fisher's linear discriminant, and proposes an intuitive nonlinear cluster transformation (NCT) that can be used for increasing the intercluster distances within a set of data points. A generalized regression neural network (GRNN) is used to learn the functional mapping between original clusters and transformed clusters. The performance of this proposed method was tested on a benchmark database and then on a real world database of patterns generated for odor identification. Initial results using NCT have been very promising.