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Dimension reduction a guided tour





Australian Computer Society, 2004.
For projective methods, we review projection pursuit, principal component analysis (PCA kernel PCA, probabilistic PCA, canonical correlation analysis (CCA kernel CCA, Fisher discriminant analysis, oriented PCA, and several techniques for sufficient dimension reduction.Touretzky, editor, Advances in Neural Information Processing Systems II, San Mateo, CA, 1990.Cohn, editors, Advances in Neural Information Processing Systems, volume 11, pages 382388, Cambridge, MA, 1999.Ieee Transactions on Pattern Analysis and Machine Intelligence, 22(8 888905, 2000.Biclustering of expression data using simulated annealing.Ieee Transactions on Speech and Audio Processing, 11(3 165174, 2003.In Fifth Annual Workshop on Computational Learning Theory, pages 144152, Pittsburgh, 1992.There are three strategies: the filter strategy (e.g.In this chapter an overview of dimension reduction techniques based on this organization is presented and the important techniques in each category are described.Cohn, editors, 82 Christopher.C.Shasha, D High (2004) Performance Discovery in Time Series Berlin: Springer.Dimension reduction can be beneficial not only for reasons of computational efficiency but also because it can improve the accuracy of the analysis.Statistical Factor Analysis and Related Methods.Kearns, and Sara.Local Fisher discriminant analysis for supervised dimensionality reduction.Journal of Machine Learning Research, 3:13711382, 2003.Journal of the American Statistical Association, resultat concours douane ci 2018 84(406 502516, 1989.Ieee Transactions on Computers, c-23(9 881890, 1974.In Proceedings of the International Conference on Machine Learning, 2004.Convolutional networks for images, speech and time-series.Doi :.1007/.
Feature subset selection in text-learning.


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