The role of multiple, linear-projection based visualization techniques in RBF-based classification of high dimensional data

The paper presents a method for the 3D visualization of the structure of radial basis function networks using traditional and novel methods of dimensionality reduction. This method allows the visualization of basis function characteristics (centers and widths) along with second level weights. To facilitate the interpretation of a wide variety of high dimensional problems, several forms of projections into 20 or 30 spaces can be used interactively. The traditional methods of principal component analysis and Fisher's linear discriminant are used as well as a novel linear projection method.