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Simbed: similarity-based embedding

  1. Demartines P., Herault J., Curvilinear component analysis: a self-organizing neural network for nonlinear mapping of data sets, 10.1109/72.554199
  2. Evans, M., Hastings, N., Peacock, B.: Statistical Distributions, 3rd edn., New York (2000)
  3. Francois Damien, Wertz Vincent, Verleysen Michel, The Concentration of Fractional Distances, 10.1109/tkde.2007.1037
  4. Hérault, J., Jaussions-Picaud, C., Guérin-Dugué, A.: Curvilinear component analysis for high dimensional data representation: I. Theoretical aspects and practical use in the presence of noise. In: Mira, J., Sánchez, J.V. (eds.) Proceedings of IWANN 1999, vol. II, pp. 635–644. Springer, Alicante (1999)
  5. Hinton, G., Roweis, S.T.: Stochastic neighbor embedding. In: Becker, S., Thrun, S., Obermayer, K. (eds.) Advances in Neural Information Processing Systems (NIPS 2002), vol. 15, pp. 833–840. MIT Press, Cambridge (2003)
  6. Kohonen Teuvo, Self-organized formation of topologically correct feature maps, 10.1007/bf00337288
  7. Kramer Mark A., Nonlinear principal component analysis using autoassociative neural networks, 10.1002/aic.690370209
  8. Kruskal J. B., Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis, 10.1007/bf02289565
  9. Lee John Aldo, Lendasse Amaury, Verleysen Michel, Nonlinear projection with curvilinear distances: Isomap versus curvilinear distance analysis, 10.1016/j.neucom.2004.01.007
  10. Nonlinear Dimensionality Reduction, ISBN:9780387393506, 10.1007/978-0-387-39351-3
  11. Lee John A., Verleysen Michel, Quality assessment of dimensionality reduction: Rank-based criteria, 10.1016/j.neucom.2008.12.017
  12. Pearson Karl, LIII.On lines and planes of closest fit to systems of points in space, 10.1080/14786440109462720
  13. Robbins Herbert, Monro Sutton, A Stochastic Approximation Method, 10.1214/aoms/1177729586
  14. Roweis S. T., Nonlinear Dimensionality Reduction by Locally Linear Embedding, 10.1126/science.290.5500.2323
  15. Sammon J.W., A Nonlinear Mapping for Data Structure Analysis, 10.1109/t-c.1969.222678
  16. Saul, L.K., Weinberger, K.Q., Ham, J.H., Sha, F., Lee, D.D.: Spectral methods for dimensionality reduction. In: Chapelle, O., Schoelkopf, B., Zien, A. (eds.) Semisupervised Learning. MIT Press, Cambridge (2006)
  17. Schölkopf Bernhard, Smola Alexander, Müller Klaus-Robert, Nonlinear Component Analysis as a Kernel Eigenvalue Problem, 10.1162/089976698300017467
  18. Shepard Roger N., The analysis of proximities: Multidimensional scaling with an unknown distance function. I., 10.1007/bf02289630
  19. Tenenbaum J. B., A Global Geometric Framework for Nonlinear Dimensionality Reduction, 10.1126/science.290.5500.2319
  20. van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. Journal of Machine Learning Research 9, 2579–2605 (2008)
  21. Young Gale, Householder A. S., Discussion of a set of points in terms of their mutual distances, 10.1007/bf02287916
Bibliographic reference Lee, John Aldo ; Verleysen, Michel. Simbed: similarity-based embedding.19th International Conference on Artificial Neural Networks (ICANN 2009) (Limassol (Cyprus), du 14/09/2009 au 17/09/2009). In: Alippi, C.; Polycarpou, M.; Ellinas, G.; Panayiotou, C.;, Lecture Notes in Computer Science, Springer verlag : Berlin-Heidelberg2009, p.95-104
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