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Comparing and Exploring High-Dimensional Data with Dimensionality Reduction Algorithms and Matrix Visualizations

R. Cutura, M. Aupetit, J. Fekete, and M. Sedlmair. AVI '20 : Proceedings of the International Conference on Advanced Visual Interfaces, page 10. New York, Association for Computing Machinery, (2020)
DOI: 10.1145/3399715.3399875

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High-dimensional labeled data analysis with topology representing graphs.M. Aupetit, and T. Catz. Neurocomputing, (2005)Robust Topology Representing Networks.M. Aupetit. ESANN, page 45-50. (2003)Learning topology of a labeled data set with the supervised generative gaussian graph.P. Gaillard, M. Aupetit, and G. Govaert. ESANN, page 235-240. (2007)Mapping without visualizing local default is nonsense.S. Lespinats, and M. Aupetit. ESANN, (2010)Recent Advances in Nonlinear Dimensionality Reduction, Manifold and Topological Learning.A. Wismüller, M. Verleysen, M. Aupetit, and J. Lee. ESANN, (2010)Visualizing distortions in continuous projection techniques.M. Aupetit. ESANN, page 465-470. (2004)Extraction des nombres de Betti avec un modèle génératif.M. Maillot, M. Aupetit, and G. Govaert. EGC, volume RNTI-E-24 of Revue des Nouvelles Technologies de l'Information, page 97-102. Hermann-Éditions, (2013)A multidimensional brush for scatterplot data analytics.M. Aupetit, N. Heulot, and J. Fekete. IEEE VAST, page 221-222. IEEE Computer Society, (2014)VisCoDeR: A tool for visually comparing dimensionality reduction algorithms.R. Cutura, S. Holzer, M. Aupetit, and M. Sedlmair. ESANN, (2018)Concerning the differentiability of the energy function in vector quantization algorithmsD. Lepetz, M. Némoz-Gaillard, and M. Aupetit. CoRR, (2006)