Abstract
Nonlinear dimensionality reduction (DR) techniques offer the possibility to visually inspect a given finite high-dimensional data set in two dimensions. In this contribution, we address the problem to visualize a trained classifier on top of these projections. We investigate the suitability of popular DR techniques for this purpose and we point out the benefit of integrating auxiliary information as provided by the classifier into the pipeline based on the Fisher information.
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Schulz, A., Gisbrecht, A., Hammer, B. (2013). Using Nonlinear Dimensionality Reduction to Visualize Classifiers. In: Rojas, I., Joya, G., Gabestany, J. (eds) Advances in Computational Intelligence. IWANN 2013. Lecture Notes in Computer Science, vol 7902. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38679-4_4
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DOI: https://doi.org/10.1007/978-3-642-38679-4_4
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