Multi-category support vector machines, feature selection and solution path

Lifeng Wang, Xiaotong T Shen

Research output: Contribution to journalArticlepeer-review

21 Scopus citations


Support Vector Machines (SVMs) have proven to deliver high performance. However, problems remain with respect to feature selection in multicategory classification. In this article, we propose an algorithm to compute an entire regularization solution path for adaptive feature selection via L 1-norm penalized multi-category MSVM (L1MSVM). The advantages of this algorithm are three-fold. First, it permits fast computation for fine tuning, which yields accurate prediction. Second, it greatly reduces the cost of memory. This is especially important in genome classification, where a linear program with tens of thousands of variables has to be solved. Third, it yields a selection order in which the features can be examined sequentially. The performance of the proposed algorithm is examined in simulations and with data.

Original languageEnglish (US)
Pages (from-to)617-633
Number of pages17
JournalStatistica Sinica
Issue number2
StatePublished - Apr 1 2006


  • Genome classification
  • Hinge loss
  • L -norm
  • Penalty with
  • Regularization


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