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Abstract: A novel approach to combining clustering and feature selection is presented. (Update)
Cited by: More
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BibTeX entry: (Update)
V. Roth and T. Lange. Feature selection in clustering problems. In Advances in Neural Information Processing Systems 16. MIT Press, Cambridge, MA, 2004. http://citeseer.ist.psu.edu/roth03feature.html More
@misc{ roth04feature,
author = "V. Roth and T. Lange",
title = "Feature selection in clustering problems",
text = "V. Roth and T. Lange. Feature selection in clustering problems. In Advances
in Neural Information Processing Systems 16. MIT Press, Cambridge, MA, 2004.",
year = "2004",
url = "citeseer.ist.psu.edu/roth03feature.html" }
Citations (may not include all citations):2528 Maximum likelihood from incomplete data via the EM algorithm (context) - Dempster, Laird et al. - 1977
83 Pairwise data clustering by deterministic annealing - Hofmann, Buhmann - 1997
83 Regression shrinkage and selection via the lasso - Tibshirani - 1996
57 Discriminant analysis by gaussian mixtures - Hastie, Tibshirani - 1996
45 Flexible discriminant analysis by optimal scoring - Hastie, Tibshirani et al. - 1994
36 Penalized discriminant analysis - Hastie, Buja et al. - 1995
19 Bayesian non-linear modelling for the prediction competition - MacKay - 1994
9 Class discovery in gene expression data (context) - Ben-Dor, Friedman et al. - 2001
8 Bayesian learning of sparse classifiers - Figueiredo, Jain
7 the lasso and its dual - Osborne, Presnell et al. - 2000
5 Going metric: Denoising pairwise data - Roth, Laub et al. - 2003
3 Feature selection in mixture-based clustering - Law, Jain et al. - 2003
2 Stability-based model selection - Lange, Braun et al. - 2003
2 Estimating the reliability of ICA projections - Meinecke, Ziehe et al. - 2002
1 Identifying splits with clear separation: a new class discov.. (context) - Heydebreck, Huber et al. - 2001
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