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Erschienen in: International Journal of Machine Learning and Cybernetics 6/2013

01.12.2013 | Original Article

Proximal support tensor machines

verfasst von: Reshma Khemchandani, Anuj Karpatne, Suresh Chandra

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 6/2013

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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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Metadaten
Titel
Proximal support tensor machines
verfasst von
Reshma Khemchandani
Anuj Karpatne
Suresh Chandra
Publikationsdatum
01.12.2013
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 6/2013
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-012-0132-6

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