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IJMLC 2020 Vol.10(4): 527-533 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2020.10.4.968

Novelty Detection in Multimodal Datasets Based on Least Square Probabilistic Analysis

Hiroyuki Yoda, Akira Imakura, Momo Matsuda, Xiucai Ye, and Tetsuya Sakurai

Abstract—Novelty detection represents the detection of anomalous data based on a training set consisting of only the normal data. In this study, we propose a new probabilistic approach for novelty detection to effectively detect anomalous data, particularly for the case of multimodal training dataset. Our method is inspired by the Least-Squares Probabilistic Classifier (LSPC), which is an efficient multi-class classification method. Numerical experimental results based on multimodal datasets show that the proposed method outperforms the related methods.

Index Terms—Novelty detection, multimodal datasets, least-square probabilistic analysis.

The authors are with University of Tsukuba, Ibaraki, Japan (e-mail: yoda.hiroyuki.wy@alumni.tsukuba.ac.jp, imakura@cs.tsukuba.ac.jp, matsuda.momo.ww@alumni.tsukuba.ac.jp, yexiucai2013@gmail.com, sakurai@cs.tsukuba.ac.jp).

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Cite: Hiroyuki Yoda, Akira Imakura, Momo Matsuda, Xiucai Ye, and Tetsuya Sakurai, "Novelty Detection in Multimodal Datasets Based on Least Square Probabilistic Analysis," International Journal of Machine Learning and Computing vol. 10, no. 4, pp. 527-533, 2020.

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net


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