Proximal support tensor machines

Reshma Khemchandani, Anuj Karpatne, Suresh Chandra

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

Abstract

To utilize the structural information present in multidimensional features of an object, a tensor-based learning framework, termed as support tensor machines (STMs), was developed on the lines of support vector machines. In order to improve it further we have developed a least squares variant of STM, termed as proximal support tensor machine (PSTM), where the classifier is obtained by solving a system of linear equations rather than a quadratic programming problem at each iteration of PSTM algorithm as compared to STM algorithm. This in turn provides a significant reduction in the computation time, as well as comparable classification accuracy. The efficacy of the proposed method has been demonstrated in simulations over face detection and handwriting recognition datasets.

Original languageEnglish (US)
Pages (from-to)703-712
Number of pages10
JournalInternational Journal of Machine Learning and Cybernetics
Volume4
Issue number6
DOIs
StatePublished - Dec 1 2013

Keywords

  • Alternating projection
  • Proximal support vector machines
  • Support tensor machines

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