Visual exploration of production data using small multiples design with non-uniform color mapping
Computers and Industrial Engineering
Non-Rigid Image Registration by Neural Computation
Journal of VLSI Signal Processing Systems
Discriminatory mining of gene expression microarray data
Journal of VLSI Signal Processing Systems - Special issue on signal processing and neural networks for bioinformatics
Hierarchical Feature Extraction for Compact Representation and Classification of Datasets
Neural Information Processing
Local matrix adaptation in topographic neural maps
Neurocomputing
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Visual exploration has proven to be a powerful tool for multivariate data mining and knowledge discovery. Most visualization algorithms aim to find a projection from the data space down to a visually perceivable rendering space. To reveal all of the interesting aspects of multimodal data sets living in a high-dimensional space, a hierarchical visualization algorithm is introduced which allows the complete data set to be visualized at the top level, with clusters and subclusters of data points visualized at deeper levels. The methods involve hierarchical use of standard finite normal mixtures and probabilistic principal component projections, whose parameters are estimated using the expectation-maximization and principal component neural networks under the information theoretic criteria. We demonstrate the principle of the approach on several multimodal numerical data sets, and we then apply the method to the visual explanation in computer-aided diagnosis for breast cancer detection from digital mammograms