Field Support Vector Machines

Kaizhu Huang, Haochuan Jiang*, Xu Yao Zhang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

31 Citations (Scopus)

Abstract

Conventional classifiers often regard input samples as identically and independently distributed (i.i.d.). This is however not true in many real applications, especially when patterns occur as groups (where each group shares a homogeneous style). Such tasks are also called field classification. By breaking the i.i.d. assumption, one novel framework called Field Support Vector Machine (F-SVM) is proposed in this paper. The distinction lies that it is capable of training and predicting a group of patterns (i.e., a field pattern) simultaneously. Specifically, the proposed F-SVM classifier is investigated by learning simultaneously both the classifier and the Style Normalization Transformation for each group of data (called field). Such joint learning proves even feasible in the high-dimensional kernel space. An efficient optimization algorithm is further developed with the convergence guaranteed. More importantly, by appropriately exploring the style consistency in each field, the F-SVM is able to significantly improve the classification accuracy. A series of experiments are conducted to verify the effectiveness of the F-SVM model. Empirical results show that the proposed F-SVM achieves in three different benchmark data sets the best performance so far, significantly better than those state-of-the-art classifiers.

Original languageEnglish
Article number8116699
Pages (from-to)454-463
Number of pages10
JournalIEEE Transactions on Emerging Topics in Computational Intelligence
Volume1
Issue number6
DOIs
Publication statusPublished - Dec 2017

Keywords

  • Field classification
  • style normalization
  • support vector machine
  • transfer learning

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