Dimensionality Reduction via Sparse Support Vector Machines

Dimensionality Reduction via Sparse Support Vector Machines (2003)

Cached

Download Links

Other Repositories/Bibliography

CiteULike
DBLP

by Jinbo Bi , Kristin P. Bennett , Mark Embrechts , Curt M. Breneman , Minghu Song , Isabelle Guyon , André Elisseeff

Venue:	Journal of Machine Learning Research
Citations:	45 - 12 self

BibTeX

@ARTICLE{Bi03dimensionalityreduction,
    author = {Jinbo Bi and Kristin P. Bennett and Mark Embrechts and Curt M. Breneman and Minghu Song and Isabelle Guyon and André Elisseeff},
    title = {Dimensionality Reduction via Sparse Support Vector Machines},
    journal = {Journal of Machine Learning Research},
    year = {2003},
    volume = {3},
    pages = {2003}
}

Years of Citing Articles

Bookmark

OpenURL

Abstract

We describe a methodology for performing variable ranking and selection using support vector machines (SVMs). The method constructs a series of sparse linear SVMs to generate linear models that can generalize well, and uses a subset of nonzero weighted variables found by the linear models to produce a final nonlinear model. The method exploits the fact that a linear SVM (no kernels) with # 1 -norm regularization inherently performs variable selection as a side-e#ect of minimizing capacity of the SVM model. The distribution of the linear model weights provides a mechanism for ranking and interpreting the e#ects of variables.

Citations

6696	The Nature of Statistical Learning Theory - Vapnik - 1995
1998	Bagging predictors - Breiman - 1996
1512	Smola A: Learning with Kernels - Schölkopf - 2002
1089	Atomic decomposition by basis pursuit - Chen, Donoho, et al. - 1998
944	J: General Linear Models - McCullagh, Nelder - 1989
933	A training algorithm for optimal margin classifiers - Boser, Guyon, et al. - 1992
775	G: Wrappers for Feature Subset Selection - Kohavi, John - 1996
474	Gene selection for cancer classification using support vector machines - Guyon, Weston, et al. - 2002
242	Modern Applied Statistics with S-PLUS - Venables, Ripley - 1999
230	New support vector algorithms - Schölkopf, Smola, et al. - 2000
183	AJ: Subset selection in regression - Miller - 1990
163	Feature Selection for SVMs - Weston, Mukherjee, et al. - 2000
113	Prediction games and arcing algorithms - Breiman - 1997
77	Hedonic prices and the demand for clean air - Harrison, Rubinfield - 1978
68	Feature selection and extraction - Kittler - 1986
55	A linear programming approach to novelty detection - Campbell, Bennett - 2000
51	Combining support vector and mathematical programming methods for classi cation - Bennett - 1999
28	Ranking a random feature for variable and feature selection - STOPPIGLIA, DREYFUS, et al.
24	Linear Programs for Automatic Accuracy Control - Smola, Scholkopf, et al. - 1999
22	A pattern search method for model selection of support vector regression - Momma, Bennett - 2002
19	Regression Selection and Shrinkage Via the Lasso - Tibshirani - 1995
18	Geometry in learning - Bennett, Bredensteiner - 1998
18	Parsimonious Least Norm Approximation - Bradley, Mangasarian - 1998
13	Derivative-free pattern search methods for multidisciplinary design problems - Dennis, Torczon - 1994
10	Feature Subset Selection by Population-Based Incremental Learning - Inza, Merino, et al. - 1999
6	Prediction of protein retention times in anion-exchange chromatography systems using support vector regression - Song, Breneman, et al. - 2002
5	Bagging neural network sensitivity analysis for feature reduction in qsar problems - Embrechts - 2001
4	Descriptor generation, selection and model building in quantitative structure-property analysis - Breneman, Bennett, et al. - 2002
2	Comparison of classifier-specific feature selection algorithms - Kudo, Somol, et al. - 2000
1	Descriptor generation, selection and model building in quantitative structure-property analysis - Bi, Embrechts, et al. - 2002
1	Bagging neural network sensitivity analysis for feature reduction in QSAR problems - Washington - 2001