Feature extraction of underground nuclear explosions based on NMF and KNMF

Authors:
Gang Liu;Xi-Hai Li;Dai-Zhi Liu;Wei-Gang Zhai
Affiliations:
Xi’an Research Institute of High Technology, Hongqing Town, Baqiao, Xi’an, People’s Republic of China;Xi’an Research Institute of High Technology, Hongqing Town, Baqiao, Xi’an, People’s Republic of China;Xi’an Research Institute of High Technology, Hongqing Town, Baqiao, Xi’an, People’s Republic of China;Xi’an Research Institute of High Technology, Hongqing Town, Baqiao, Xi’an, People’s Republic of China
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Year:
2006

Citing 3
Cited 0

A training algorithm for optimal margin classifiers

COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
The nature of statistical learning theory

The nature of statistical learning theory
Nonlinear component analysis as a kernel eigenvalue problem

Neural Computation

Quantified Score

Hi-index	0.00

Visualization

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.