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Regularized supervised novelty detection and its application in activity monitoring

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Abstract

In some real applications, it requires to recognize known class(es) as well as unknown class. Traditional classification can only recognize the known class(es) which occur(s) in the training set. Recently, Kernel Null Foley-Sammon Transformation (KNFST) is proposed to overcome the weakness that traditional classification cannot identify unknown class. In KNFST, the samples from the same class are mapped to a point via null projected directions. For a test sample, the predicted label is the class with the minimum distance to it in the projected space. If the test sample is far away from all projected points, it comes from an unknown class. However, KNFST only considers the global information which is depicted by between- and within- class scatter matrices. When the internal structure is complex, KNFST may fail. In order to handle this issue, this paper proposes a Kernel Null Regularized Foley-Sammon Transformation (KNRFST) by introducing a regularized term into KNFST. The regularized term is depicted by a neighborhood which consists of reverse nearest neighbors. The neighborhood can depict the local information in the class. The experimental results, performed on several datasets, show that KNRFST is superior to some previous methods, including KNFST, Local KNFST, WOC-SVM, Deep SVDD and OCC-GAN.

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Notes

  1. k(xi, xj) =  exp (2κHIK(xi, xj) − κHIK(xi, xi) − κHIK(xj, xj)).

  2. These datasets are collected from https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/

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Authors and Affiliations

  1. School of Computer Science and Engineering, Northeastern University, Shenyang, 110819, People’s Republic of China

    Xiangze Li

  2. Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang, 110168, People’s Republic of China

    Baoming Pu

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  1. Xiangze Li
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  2. Baoming Pu
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Correspondence to Xiangze Li.

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Li, X., Pu, B. Regularized supervised novelty detection and its application in activity monitoring. Appl Intell 53, 4813–4826 (2023). https://doi.org/10.1007/s10489-022-03782-z

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