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On the effects of dimensionality on data analysis with neural networks

Bibliographic reference Verleysen, Michel ; François, Damien ; Simon, Geoffroy ; Wertz, Vincent. On the effects of dimensionality on data analysis with neural networks.7th International Work Conference on Artificial and Natural Neural Networks (IWANN 2003) (Menorca (Spain), du 03/06/2003 au 06/06/2003). In: Lecture Notes in Computer Science, Vol. 2687, p. 105-112 (2003)In: J. Mira, J.R. Alvarez, Computational Methods in Neural Modeling, Springer-Verlag : Berlin-Heidelberg2003
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  1. Scott, D.W., Thompson, J. R.: Probability density estimation in higher dimensions. In: Douglas, S.R. (ed): Computer Science and Statistics. Proceedings of the Fifteenth Symposium on the Interface, North Holland-Elsevier, Amsterdam, New York, Oxford (1983) 173–179
  2. Demartines, P.: Analyse de données par réseaux de neurones auto-organisés. Ph.D. dissertation (in French), Institut National Polytechnique de Grenoble-France (1994)
  3. Aggarwal Charu C., Hinneburg Alexander, Keim Daniel A., On the Surprising Behavior of Distance Metrics in High Dimensional Space, Database Theory — ICDT 2001 (2001) ISBN:9783540414568 p.420-434, 10.1007/3-540-44503-x_27
  4. Beyer Kevin, Goldstein Jonathan, Ramakrishnan Raghu, Shaft Uri, When Is “Nearest Neighbor” Meaningful?, Lecture Notes in Computer Science (1999) ISBN:9783540654520 p.217-235, 10.1007/3-540-49257-7_15
  5. Bellman Richard E., Adaptive Control Processes : A Guided Tour, ISBN:9781400874668, 10.1515/9781400874668
  6. Silverman B. W., Density Estimation for Statistics and Data Analysis, ISBN:9780412246203, 10.1007/978-1-4899-3324-9
  7. Fukunaga K.: Introduction to Statistical Pattern Recognition. Academic Press, Boston, MA, (1990)
  8. Hérault, J., Guérin-Dugué, A., Villemain, P.: Searching for the embedded manifolds in highdimensional data, problems and unsolved questions. Proceedings of ESANN’2002—European Symposium on Artificial Neural Networks, d-side public, Bruges-Belgium (2002) 173–184
  9. Steinbach, M., Ertoz, L., Kumar, V.: Challenges of clustering high dimensional data. New Vistas in Statistical Physics—Applications in Econo-physics, Bioinformatics, and Pattern Recognition, Springer-Verlag (2003)
  10. Verleysen, M.: Learning high-dimensional data. Acc. for public. in Ablameyko, S., Goras, L., Gori, M., Piuri, V. (eds): Limitations and future trends in neural computation, IOS Press.
  11. Shepard Roger N., The analysis of proximities: Multidimensional scaling with an unknown distance function. I., 10.1007/bf02289630
  12. Shepard, R.N, Carroll, J.D: Parametric representation of nonlinear data structures. In P. R. Krishnaiah (ed.): International Symposium on Multivariate Analysis, Academic Press, (1965) 561–592
  13. Sammon J.W., A Nonlinear Mapping for Data Structure Analysis, 10.1109/t-c.1969.222678
  14. Demartines P., Herault J., Curvilinear component analysis: a self-organizing neural network for nonlinear mapping of data sets, 10.1109/72.554199
  15. Lee, J. A., Lendasse, A., Verleysen, M: Curvilinear Distance Analysis versus Isomap. In: Proceedings of ESANN’2002, 10th European Symposium on Artificial Neural Networks, dside public, Bruges—Belgium, (2002) 185–192
  16. Lendasse Amaury, Lee John, de Bodt �ric, Wertz Vincent, Verleysen Michel, Dimension reduction of technical indicators for the prediction of financial time series - Application to the BEL20 Market Index, 10.1051/ejess:2001114