A Robust Regularization Path Algorithm for ν-Support Vector Classification

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Abstract

The μ-support vector classification has the advantage of using a regularization parameter μto control the number of support vectors and margin errors. Recently, a regularization path algorithm for μ-support vector classification (μ-SvcPath) suffers exceptions and singularities in some special cases. In this brief, we first present a new equivalent dual formulation for μ-SVC and, then, propose a robust μ-SvcPath, based on lower upper decomposition with partial pivoting. Theoretical analysis and experimental results verify that our proposed robust regularization path algorithm can avoid the exceptions completely, handle the singularities in the key matrix, and fit the entire solution path in a finite number of steps. Experimental results also show that our proposed algorithm fits the entire solution path with fewer steps and less running time than original one does.

Original languageEnglish
Article number7419254
Pages (from-to)1241-1248
Number of pages8
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume28
Issue number5
DOIs
StatePublished - May 2017

Keywords

  • Finite convergence
  • Lower upper decomposition
  • Solution path
  • μ-support vector classification (v-SVC)

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