A surface representation approach for novelty detection

Yuhua Li

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    4 Citations (Scopus)

    Abstract

    There has been a pronounced increase in novelty detection research in recent years due to the driving force from applications such as monitoring of safety-critical systems and detection of novel objects in image sequences. This paper presents a novelty detection method from a new perspective by analysing the fundamental properties of novelty detectors. It constructs closed decision surface around the given data from known classes through the derivation of surface normal vectors and the identification of extreme patterns. A novel pattern is detected if it locates outside the region formed by the closed data surface. The experimental results demonstrate that the proposed method performs with high accuracies in detecting novel class as well as identifying known classes.
    LanguageEnglish
    Title of host publicationUnknown Host Publication
    Pages1464-1468
    Number of pages5
    Publication statusPublished - Jun 2008
    EventIEEE International Conference on Information and Automation - Zhangjiajie, China
    Duration: 1 Jun 2008 → …

    Conference

    ConferenceIEEE International Conference on Information and Automation
    Period1/06/08 → …

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    Cite this

    Li, Y. (2008). A surface representation approach for novelty detection. In Unknown Host Publication (pp. 1464-1468)
    Li, Yuhua. / A surface representation approach for novelty detection. Unknown Host Publication. 2008. pp. 1464-1468
    @inproceedings{a8ae40d26ece44348a7daf08c93bd458,
    title = "A surface representation approach for novelty detection",
    abstract = "There has been a pronounced increase in novelty detection research in recent years due to the driving force from applications such as monitoring of safety-critical systems and detection of novel objects in image sequences. This paper presents a novelty detection method from a new perspective by analysing the fundamental properties of novelty detectors. It constructs closed decision surface around the given data from known classes through the derivation of surface normal vectors and the identification of extreme patterns. A novel pattern is detected if it locates outside the region formed by the closed data surface. The experimental results demonstrate that the proposed method performs with high accuracies in detecting novel class as well as identifying known classes.",
    author = "Yuhua Li",
    note = "Reference text: [1] K. Fukunaga and L.D. Hostetler, “Estimation of gradient of a density-function, with applications in pattern-recognition,” IEEE Transactions on Information Theory, vol. 21, no. 1, pp. 32-40, 1975. [2] P. Hayton, S. Utete, D. King, S. King, P. Anuzis, and L. Tarassenko, “Static and dynamic novelty detection methods for jet engine health monitoring,” Philosophical Transactions of The Royal Society A-Mathematical Physical and Engineering Sciences, vol. 365, no. 1851, pp. 493-514, 2007. [3] P.L. Hsu, K.L. Lin, and L.C. Shen, “Diagnosis of multiple sensor and actuator failures in automotive engines,” IEEE Transactions on Vehicular Technology, vol. 44, no. 4, pp. 779-789, 1995. [4] M.A. Kramer and J.A. Leonard, “Diagnosis using backpropagation neural networks - analysis and criticism,” Computers & Chemical Engineering, vol. 14, no. 12, pp. 1323-1338, 1990. [5] Y. Li, M.J. Pont, and N.B. Jones, “Using MLP and RBF classifiers in embedded condition monitoring and fault diagnosis applications,” Transactions of the Institute of Measurement and Control, vol. 23, no. 5, pp. 315-343, 2001. [6] Y. Li, M.J. Pont, and N.B. Jones, “Improving the performance of radial basis function classifiers in condition monitoring and fault diagnosis applications where ‘unknown’ faults may occur,” Pattern Recognition Letters, vol. 23, no. 5, pp.569-577, 2002. [7] M. Markou and S. Singh, “Novelty detection: a review - part 1 & 2,” Signal Processing, vol. 83, no. 12, pp. 2481-2521, 2003. [8] M. Markou and S. Singh, “A neural network-based novelty detector for image sequence analysis,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 10, pp. 1664-1677, 2006. [9] P. Podsiadlo and G.W. Stachowiak, “Fast classification of engineering surfaces without surface parameters,” Tribology International, vo. 39, no. 12, pp. 1624-1633, 2006. [10] S.J. Roberts, “Novelty detection using extreme value statistics,” IEE Proceedings-Vision Image and Signal Processing, vol. 146, no. 3, pp. 124-129, 1999. [11] B. Sch{\"o}lkopf, R.C. Williamson, A.J. Smola, J.S. Taylor, and J.C. Platt “Support Vector Method for Novelty Detection,” In Advances in Neural Information Processing Systems, vol. 12, pp. 582–588, MIT Press, Cambridge, MA, 2000. [12] D.M.J. Tax and R.P.W. Duin, “Support vector data description,” Machine Learning, vol. 54, no. 1, pp. 45-66, 2004.",
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    Li, Y 2008, A surface representation approach for novelty detection. in Unknown Host Publication. pp. 1464-1468, IEEE International Conference on Information and Automation, 1/06/08.

    A surface representation approach for novelty detection. / Li, Yuhua.

    Unknown Host Publication. 2008. p. 1464-1468.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

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    N1 - Reference text: [1] K. Fukunaga and L.D. Hostetler, “Estimation of gradient of a density-function, with applications in pattern-recognition,” IEEE Transactions on Information Theory, vol. 21, no. 1, pp. 32-40, 1975. [2] P. Hayton, S. Utete, D. King, S. King, P. Anuzis, and L. Tarassenko, “Static and dynamic novelty detection methods for jet engine health monitoring,” Philosophical Transactions of The Royal Society A-Mathematical Physical and Engineering Sciences, vol. 365, no. 1851, pp. 493-514, 2007. [3] P.L. Hsu, K.L. Lin, and L.C. Shen, “Diagnosis of multiple sensor and actuator failures in automotive engines,” IEEE Transactions on Vehicular Technology, vol. 44, no. 4, pp. 779-789, 1995. [4] M.A. Kramer and J.A. Leonard, “Diagnosis using backpropagation neural networks - analysis and criticism,” Computers & Chemical Engineering, vol. 14, no. 12, pp. 1323-1338, 1990. [5] Y. Li, M.J. Pont, and N.B. Jones, “Using MLP and RBF classifiers in embedded condition monitoring and fault diagnosis applications,” Transactions of the Institute of Measurement and Control, vol. 23, no. 5, pp. 315-343, 2001. [6] Y. Li, M.J. Pont, and N.B. Jones, “Improving the performance of radial basis function classifiers in condition monitoring and fault diagnosis applications where ‘unknown’ faults may occur,” Pattern Recognition Letters, vol. 23, no. 5, pp.569-577, 2002. [7] M. Markou and S. Singh, “Novelty detection: a review - part 1 & 2,” Signal Processing, vol. 83, no. 12, pp. 2481-2521, 2003. [8] M. Markou and S. Singh, “A neural network-based novelty detector for image sequence analysis,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 10, pp. 1664-1677, 2006. [9] P. Podsiadlo and G.W. Stachowiak, “Fast classification of engineering surfaces without surface parameters,” Tribology International, vo. 39, no. 12, pp. 1624-1633, 2006. [10] S.J. Roberts, “Novelty detection using extreme value statistics,” IEE Proceedings-Vision Image and Signal Processing, vol. 146, no. 3, pp. 124-129, 1999. [11] B. Schölkopf, R.C. Williamson, A.J. Smola, J.S. Taylor, and J.C. Platt “Support Vector Method for Novelty Detection,” In Advances in Neural Information Processing Systems, vol. 12, pp. 582–588, MIT Press, Cambridge, MA, 2000. [12] D.M.J. Tax and R.P.W. Duin, “Support vector data description,” Machine Learning, vol. 54, no. 1, pp. 45-66, 2004.

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    N2 - There has been a pronounced increase in novelty detection research in recent years due to the driving force from applications such as monitoring of safety-critical systems and detection of novel objects in image sequences. This paper presents a novelty detection method from a new perspective by analysing the fundamental properties of novelty detectors. It constructs closed decision surface around the given data from known classes through the derivation of surface normal vectors and the identification of extreme patterns. A novel pattern is detected if it locates outside the region formed by the closed data surface. The experimental results demonstrate that the proposed method performs with high accuracies in detecting novel class as well as identifying known classes.

    AB - There has been a pronounced increase in novelty detection research in recent years due to the driving force from applications such as monitoring of safety-critical systems and detection of novel objects in image sequences. This paper presents a novelty detection method from a new perspective by analysing the fundamental properties of novelty detectors. It constructs closed decision surface around the given data from known classes through the derivation of surface normal vectors and the identification of extreme patterns. A novel pattern is detected if it locates outside the region formed by the closed data surface. The experimental results demonstrate that the proposed method performs with high accuracies in detecting novel class as well as identifying known classes.

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    Li Y. A surface representation approach for novelty detection. In Unknown Host Publication. 2008. p. 1464-1468