A surface representation approach for novelty detection

Yuhua Li

A surface representation approach for novelty detection

Yuhua Li

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	Unknown Host Publication
Pages	1464-1468
Number of pages	5
Publication status	Published (in print/issue) - Jun 2008
Event	IEEE International Conference on Information and Automation - Zhangjiajie, China Duration: 1 Jun 2008 → …

Conference

Conference	IEEE International Conference on Information and Automation
Period	1/06/08 → …

Bibliographical 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. 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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@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 {\textquoteleft}unknown{\textquoteright} 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.; IEEE International Conference on Information and Automation ; Conference date: 01-06-2008",

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pages = "1464--1468",

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AU - Li, Yuhua

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.

PY - 2008/6

Y1 - 2008/6

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.

M3 - Conference contribution

SP - 1464

EP - 1468

BT - Unknown Host Publication

T2 - IEEE International Conference on Information and Automation

Y2 - 1 June 2008

ER -

A surface representation approach for novelty detection

Abstract

Conference

Bibliographical note

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