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ICDM '09 Proceedings of the 9th Industrial Conference on Advances in Data Mining. Applications and Theoretical Aspects
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IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part I
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume 2
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Integrated Computer-Aided Engineering
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Discriminant functions calculated by Support Vector Machines (SVMs) define in a computationally efficient way projections of high-dimensional data on a direction perpendicular to the discriminating hyperplane. These projections may be used to estimate and display posterior probability densities . Additional directions for visualization and dimensionality reduction are created by repeating the linear discrimination process in a space orthogonal to already defined projections. This process allows for an efficient reduction of dimensionality and visualization of data, at the same time improving classification accuracy of a single discriminant function. Visualization of real and artificial data shows that transformed data may not be separable and thus linear discrimination will completely fail, but the nearest neighbor or rule-based methods in the reduced space may still provide simple and accurate solutions.