Advertisement

Fast supervised novelty detection and its application in remote sensing

  • Weiping ShiEmail author
  • Shengwen Yu
Methodologies and Application
  • 15 Downloads

Abstract

Multi-class supervised novelty detection (multi-class SND) is used for finding minor anomalies in many unknown samples when the normal samples follow a mixture of distributions. It needs to solve a quadratic programming (QP) whose size is larger than that in one-class support vector machine. In multi-class SND, one sample corresponds to \( n_{c} \) variables in QP. Here, \( n_{c} \) is the number of normal classes. Thus, it is time-consuming to solve multi-class SND directly. Fortunately, the solution of multi-class SND is only determined by minor samples which are with nonzero Lagrange multipliers. Due to the sparsity of the solution in multi-class SND, we learn multi-class SND on a small subset instead of the whole training set. The subset consists of the samples which would be with nonzero Lagrange multipliers. These samples are located near the boundary of the distributions and can be identified by the nearest neighbours’ distribution information. Our method is evaluated on two toy data sets and three hyperspectral remote sensing data sets. The experimental results demonstrate that the performance learning on the retained subset almost keeps the same as that on the whole training set. However, the training time reduces to less than one tenth of the whole training sets.

Keywords

Multi-class supervised novelty detection Critical samples Nearest neighbours’ distribution 

Notes

Funding

This study was funded by the National Natural Science Foundation of China (No. 61602221).

Compliance with ethical standards

Conflict of interest

Weiping Shi declares that he has no conflict of interest. Shengwen Yu declares that he has no conflict of interest.

Human and animal rights

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

References

  1. Breunig MM, Kriegel HP, Ng RT, Sander J (2000) LOF: identifying density-based local outliers. In: Proceedings of the ACM SIGMOD 2000 international conference on management of data. ACM, pp 93–104Google Scholar
  2. Butun I, Morgera SD, Sankar R (2014) A survey of intrusion detection systems in wireless sensor networks. IEEE Commun Surv Tutor 16(1):266–282CrossRefGoogle Scholar
  3. Das K, Schneider J (2007) Detecting anomalous records in categorical datasets. In: Proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 220–229Google Scholar
  4. De Almeida MB, de Pádua Braga A, Braga JP (2000) SVM-KM: speeding SVMs learning with a priori cluster selection and k-means. In: SBM. IEEE, p 162Google Scholar
  5. Eskin E, Arnold A, Prerau M, Portnoy L, Stolfo S (2002) A geometric framework for unsupervised anomaly detection. In: Barbará D, Jajodia S (eds) Applications of data mining in computer security. Kluwer Academic Publishers, Boston, pp 77–101CrossRefGoogle Scholar
  6. Guo G, Zhang JS (2007) Reducing examples to accelerate support vector regression. Pattern Recogn Lett 28(16):2173–2183CrossRefGoogle Scholar
  7. Hauskrecht M, Batal I, Valko M, Visweswaran S, Cooper GF, Clermont G (2013) Outlier detection for patient monitoring and alerting. J Biomed Inform 46(1):47–55CrossRefGoogle Scholar
  8. Jumutc V, Suykens JA (2014) Multi-class supervised novelty detection. IEEE Trans Pattern Anal Mach Intell 36(12):2510–2523CrossRefGoogle Scholar
  9. Koggalage R, Halgamuge S (2004) Reducing the number of training samples for fast support vector machine classification. Neural Inf Process Lett Rev 2(3):57–65Google Scholar
  10. Kriegel HP, Zimek A (2008) Angle-based outlier detection in high-dimensional data. In: Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 444–452Google Scholar
  11. Kriegel HP, Kröger P, Schubert E, Zimek A (2009). LoOP: local outlier probabilities. In: Proceedings of the 18th ACM conference on information and knowledge management. ACM, pp 1649–1652Google Scholar
  12. Li Y (2011) Selecting training points for one-class support vector machines. Pattern Recogn Lett 32(11):1517–1522CrossRefGoogle Scholar
  13. Li X, Lv J, Yi Z (2018) An efficient representation-based method for boundary point and outlier detection. IEEE Trans Neural Netw Learn Syst 29(1):51–62MathSciNetCrossRefGoogle Scholar
  14. Pham N, Pagh R (2012). A near-linear time approximation algorithm for angle-based outlier detection in high-dimensional data. In: Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 877–885Google Scholar
  15. Sanz JA, Galar M, Jurio A, Brugos A, Pagola M, Bustince H (2014) Medical diagnosis of cardiovascular diseases using an interval-valued fuzzy rule-based classification system. Appl Soft Comput 20:103–111CrossRefGoogle Scholar
  16. Schölkopf B, Platt JC, Shawe-Taylor J, Smola AJ, Williamson RC (2001) Estimating the support of a high-dimensional distribution. Neural Comput 13(7):1443–1471CrossRefzbMATHGoogle Scholar
  17. Tax DM, Duin RP (2004) Support vector data description. Mach Learn 54(1):45–66CrossRefzbMATHGoogle Scholar
  18. Wächter A, Biegler LT (2006) On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Math Program 106(1):25–57MathSciNetCrossRefzbMATHGoogle Scholar
  19. Wang D, Shi L (2008) Selecting valuable training samples for SVMs via data structure analysis. Neurocomputing 71(13–15):2772–2781CrossRefGoogle Scholar
  20. Zhu F, Yang J, Ye N, Gao C, Li G, Yin T (2014a) Neighbors’ distribution property and sample reduction for support vector machines. Appl Soft Comput 16:201–209CrossRefGoogle Scholar
  21. Zhu F, Ye N, Yu W, Xu S, Li G (2014b) Boundary detection and sample reduction for one-class support vector machines. Neurocomputing 123:166–173CrossRefGoogle Scholar
  22. Zhu F, Yang J, Gao C, Xu S, Ye N, Yin T (2016a) A weighted one-class support vector machine. Neurocomputing 189:1–10CrossRefGoogle Scholar
  23. Zhu F, Yang J, Xu S, Gao C, Ye N, Yin T (2016b) Relative density degree induced boundary detection for one-class SVM. Soft Comput 20(11):4473–4485CrossRefGoogle Scholar
  24. Zhu F, Yang J, Gao J, Xu C, Xu S, Gao C (2017) Finding the samples near the decision plane for support vector learning. Inf Sci 382:292–307CrossRefGoogle Scholar
  25. Zhu F, Gao J, Xu C, Yang J, Tao D (2018) On selecting effective patterns for fast support vector regression training. IEEE Trans Neural Netw Learn Syst 29(8):3610–3622MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.College of GeomaticsShandong University of Science and TechnologyQingdaoPeople’s Republic of China

Personalised recommendations