article

One-class support higher order tensor machine classifier

Authors:

Ping ZhongAuthors Info & Claims

Applied Intelligence, Volume 47, Issue 4

Pages 1022 - 1030

https://doi.org/10.1007/s10489-017-0945-9

Published: 01 December 2017 Publication History

Abstract

One-class classification problems have been widely encountered in the fields that the negative class patterns are difficult to be collected, and the one-class support vector machine is one of the popular algorithms for solving them. However, one-class support vector machine is a vector-based learning algorithm, and it cannot work directly when the input pattern is a tensor. This paper proposes a tensor-based maximum margin classifier for one-class classification problems, and develops a One-Class Support Higher Order Tensor Machine (HO-OCSTM) which can separate most of the target patterns from the origin with the maximum margin in the higher order tensor space. HO-OCSTM directly employs the higher order tensors as the input patterns, and it is more proper for small sample study. Moreover, the direct use of tensor representation has the advantage of retaining the structural information of data, which helps improve the generalization ability of the proposed algorithm. We implement HO-OCSTM by the alternating projection method and solve a convex quadratic programming similar to the standard one-class support vector machine algorithm at each iteration. The experimental results have shown the high recognition accuracy of the proposed method.

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Cited By

Wang HZhou Z(2021)Rough margin-based ν-twin support tensor machine in pattern recognitionJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-20057340:1(685-702)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.3233/JIFS-200573
Razzak IMoustafa NMumtaz SXu G(2021)One‐class tensor machine with randomized projection for large‐scale anomaly detection in high‐dimensional and noisy dataInternational Journal of Intelligent Systems10.1002/int.2272937:8(4515-4536)Online publication date: 8-Nov-2021
https://dl.acm.org/doi/10.1002/int.22729

One-class support higher order tensor machine classifier
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning

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Information

Published In

cover image Applied Intelligence

Applied Intelligence Volume 47, Issue 4

December 2017

289 pages

ISSN:0924-669X

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Copyright © Copyright © 2017 Springer Science+Business Media, LLC.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 December 2017

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Cited By

Wang HZhou Z(2021)Rough margin-based ν-twin support tensor machine in pattern recognitionJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-20057340:1(685-702)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.3233/JIFS-200573
Razzak IMoustafa NMumtaz SXu G(2021)One‐class tensor machine with randomized projection for large‐scale anomaly detection in high‐dimensional and noisy dataInternational Journal of Intelligent Systems10.1002/int.2272937:8(4515-4536)Online publication date: 8-Nov-2021
https://dl.acm.org/doi/10.1002/int.22729

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