ViSOM-a novel method for multivariate data projection and structure visualization

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    When used for visualization of high-dimensional data, the self-organizing map (SOM) requires a coloring scheme such as the U-matrix to mark the distances between neurons. Even so, the structures of the data clusters may not be apparent and their shapes are often distorted. In this paper, a visualization-induced SOM (ViSOM) is proposed to overcome these shortcomings. The algorithm constrains and regularizes the inter-neuron distance with a parameter that controls the resolution of the map. The mapping preserves the inter-point distances of the input data on the map as well as the topology. It produces a graded mesh in the data space such that the distances between mapped data points on the map resemble those in the original space, like in the Sammon mapping. However, unlike the Sammon mapping, the ViSOM can accommodate both training data and new arrivals and is much simpler in computational complexity. Several experimental results and comparisons with other methods are presented.
    Original languageEnglish
    Pages (from-to)237-243
    Number of pages6
    JournalIEEE Transactions on Neural Networks
    Issue number1
    Publication statusPublished - Jan 2002


    • Dimension reduction
    • Multidimensional scaling
    • Multivariate data visualization
    • Nonlinear mapping
    • Self-organizing maps (SOMs)


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