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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© 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
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