Visualization of transformed multivariate data sets with autoassociative neural networks

Artificial neural networks have recently gained prominence as powerful tools for the projection of high-dimensional data, where fast interactive mapping of multi-dimensional data onto 2D or 3D maps with as little distortion as possible is required. These methods typically generate static maps of the data, based on some optimization criterion. A new strategy based on the transformation of the data prior to use of autoassociative neural networks is therefore proposed and it is shown that this strategy allows more flexible visualization of the data than is possible with either Kohonen or hidden target backpropagation (Sammon) neural networks, in that various perspectives of the multi-dimensional space can be explored by dynamically mapping the data with respect to user-defined vantage points in the multi-dimensional space.