Electronic Journal of Statistics

Variable selection for the multicategory SVM via adaptive sup-norm regularization

Hao Helen Zhang, Yufeng Liu, Yichao Wu, and Ji Zhu

Full-text: Open access

Abstract

The Support Vector Machine (SVM) is a popular classification paradigm in machine learning and has achieved great success in real applications. However, the standard SVM can not select variables automatically and therefore its solution typically utilizes all the input variables without discrimination. This makes it difficult to identify important predictor variables, which is often one of the primary goals in data analysis. In this paper, we propose two novel types of regularization in the context of the multicategory SVM (MSVM) for simultaneous classification and variable selection. The MSVM generally requires estimation of multiple discriminating functions and applies the argmax rule for prediction. For each individual variable, we propose to characterize its importance by the supnorm of its coefficient vector associated with different functions, and then minimize the MSVM hinge loss function subject to a penalty on the sum of supnorms. To further improve the supnorm penalty, we propose the adaptive regularization, which allows different weights imposed on different variables according to their relative importance. Both types of regularization automate variable selection in the process of building classifiers, and lead to sparse multi-classifiers with enhanced interpretability and improved accuracy, especially for high dimensional low sample size data. One big advantage of the supnorm penalty is its easy implementation via standard linear programming. Several simulated examples and one real gene data analysis demonstrate the outstanding performance of the adaptive supnorm penalty in various data settings.

Article information

Source
Electron. J. Statist. Volume 2 (2008), 149-167.

Dates
First available in Project Euclid: 21 March 2008

Permanent link to this document
https://projecteuclid.org/euclid.ejs/1206123678

Digital Object Identifier
doi:10.1214/08-EJS122

Mathematical Reviews number (MathSciNet)
MR2386091

Zentralblatt MATH identifier
1135.62056

Subjects
Primary: 62H30: Classification and discrimination; cluster analysis [See also 68T10, 91C20]

Keywords
Classification L_1-norm penalty multicategory sup-norm SVM

Citation

Zhang, Hao Helen; Liu, Yufeng; Wu, Yichao; Zhu, Ji. Variable selection for the multicategory SVM via adaptive sup-norm regularization. Electron. J. Statist. 2 (2008), 149--167. doi:10.1214/08-EJS122. https://projecteuclid.org/euclid.ejs/1206123678


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