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Feature selection in the Laplacian support vector machine

Author

Listed:
  • Lee, Sangjun
  • Park, Changyi
  • Koo, Ja-Yong

Abstract

Traditional classifiers including support vector machines use only labeled data in training. However, labeled instances are often difficult, costly, or time consuming to obtain while unlabeled instances are relatively easy to collect. The goal of semi-supervised learning is to improve the classification accuracy by using unlabeled data together with a few labeled data in training classifiers. Recently, the Laplacian support vector machine has been proposed as an extension of the support vector machine to semi-supervised learning. The Laplacian support vector machine has drawbacks in its interpretability as the support vector machine has. Also it performs poorly when there are many non-informative features in the training data because the final classifier is expressed as a linear combination of informative as well as non-informative features. We introduce a variant of the Laplacian support vector machine that is capable of feature selection based on functional analysis of variance decomposition. Through synthetic and benchmark data analysis, we illustrate that our method can be a useful tool in semi-supervised learning.

Suggested Citation

  • Lee, Sangjun & Park, Changyi & Koo, Ja-Yong, 2011. "Feature selection in the Laplacian support vector machine," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 567-577, January.
  • Handle: RePEc:eee:csdana:v:55:y:2011:i:1:p:567-577
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    References listed on IDEAS

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    1. Yoonkyung Lee & Yuwon Kim & Sangjun Lee & Ja-Yong Koo, 2006. "Structured multicategory support vector machines with analysis of variance decomposition," Biometrika, Biometrika Trust, vol. 93(3), pages 555-571, September.
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    Cited by:

    1. Park, Beomjin & Park, Changyi, 2023. "Multiclass Laplacian support vector machine with functional analysis of variance decomposition," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).

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