Abstract
Projecting and visualizing objects in a two- or tree-dimension space is a standard data analysis task. In addition to this visualization it might be of interest to allow the user to add knowledge in the form of (di)similarity constraints among objects, when those appear either too close or too far in the observation space. In this paper we propose three kinds of constraints and present a resolution method that derives from PCA. Experiments have been performed with both synthetic and usual datasets. They show that a relevant representation can be achieved with a limited set of constraints.
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Martin, L., Exbrayat, M., Cleuziou, G., Moal, F. (2012). Interactive and Progressive Constraint Definition for Dimensionality Reduction and Visualization. In: Guillet, F., Ritschard, G., Zighed, D. (eds) Advances in Knowledge Discovery and Management. Studies in Computational Intelligence, vol 398. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25838-1_7
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DOI: https://doi.org/10.1007/978-3-642-25838-1_7
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