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Proximal support tensor machines

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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.

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Authors and Affiliations

  1. RBS India Development Centre, Gurgaon, Haryana, 122002, India

    Reshma Khemchandani

  2. Department of Mathematics, Indian Institute of Technology, Hauz Khas, New Delhi, 110016, India

    Anuj Karpatne & Suresh Chandra

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  1. Reshma Khemchandani
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  2. Anuj Karpatne
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  3. Suresh Chandra
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Correspondence to Reshma Khemchandani.

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Khemchandani, R., Karpatne, A. & Chandra, S. Proximal support tensor machines. Int. J. Mach. Learn. & Cyber. 4, 703–712 (2013). https://doi.org/10.1007/s13042-012-0132-6

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  • DOI: https://doi.org/10.1007/s13042-012-0132-6

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