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Feature Extraction of Underground Nuclear Explosions Based on NMF and KNMF

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Advances in Neural Networks - ISNN 2006 (ISNN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3971))

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Abstract

Non-negative matrix factorization (NMF) is a recently proposed parts-based representation method, and because of its non-negativity constraints, it is mostly used to learn parts of faces and semantic features of text. In this paper, non-negative matrix factorization is first applied to extract features of underground nuclear explosion signals and natural earthquake signals, then a novel kernel-based non-negative matrix factorization (KNMF) method is proposed and also applied to extract features. To compare practical classification ability of these features extracted by NMF and KNMF, linear support vector machine (LSVM) is applied to distinguish nuclear explosions from natural earthquakes. Theoretical analysis and practical experimental results indicate that kernel-based non-negative matrix factorization is more appropriate for the feature extraction of underground nuclear explosions and natural earthquakes.

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References

  1. Douglas, A.: Seismic Source Identification: A Review of Past and Present Research Efforts. Lecture Notes of Dr. David Booth, Zhang Jiajie (2001)

    Google Scholar 

  2. Lee, D.D., Seung, H.S.: Learning Parts of Objects by Non-negative Matrix Factorization. Nature 401, 788–791 (1999)

    Article  Google Scholar 

  3. Lee, D.D., Seung, H.S.: Algorithms for Non-negative Matrix Factorization. In: Proceedings of Advanced Neural Information Processing Systems, vol. 13, pp. 556–562 (2001)

    Google Scholar 

  4. Boser, B.E., Guyon, I.M., Vapnik, V.N.: A Training Algorithm for Optimal Margin Classifiers. In: Haussler, D. (ed.) Porceedings of the 5th Annual ACM Workshop on Computational Learning Therory, pp. 144–152 (1992)

    Google Scholar 

  5. Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, New York (1995)

    MATH  Google Scholar 

  6. Schölkopf, B., Smola, A.J., Müller, K.R.: Nonlinear Component Analysis as A Kernel Eigenvalue Problem. Neural Computation 10, 1299–1319 (1998)

    Article  Google Scholar 

  7. Mika, S., Rötsch, G., Weston, J., Schölkopf, B., Müller, K.R.: Fisher Discriminant Analysis With Kernels. In: Hu, Y.H., Larsen, J., Wilson, E., Gouglas, S. (eds.) Neural Networks for Signal Processing IX, pp. 41–48 (1999)

    Google Scholar 

  8. Saitoh, S.: Theory of Reporducing Kernels and Its Applications. Longman, Harlow (1998)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Xi’an Research Institute of High Technology, Hongqing Town, Baqiao, Xi’an, 710025, People’s Republic of China

    Gang Liu, Xi-Hai Li, Dai-Zhi Liu & Wei-Gang Zhai

Authors
  1. Gang Liu
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  2. Xi-Hai Li
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  3. Dai-Zhi Liu
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  4. Wei-Gang Zhai
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

  3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Zurada

  4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd., 200240, Shanghai, China

    Bao-Liang Lu

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

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© 2006 Springer-Verlag Berlin Heidelberg

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Liu, G., Li, XH., Liu, DZ., Zhai, WG. (2006). Feature Extraction of Underground Nuclear Explosions Based on NMF and KNMF. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3971. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11759966_208

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  • DOI: https://doi.org/10.1007/11759966_208

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34439-1

  • Online ISBN: 978-3-540-34440-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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