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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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