基于Isomap的核爆地震模式识别

Pattern Recognition of Underground Nuclear Explosions and Natural Earthquakes Based on Isomap

本文利用Isomap算法对核爆地震和天然地震的特征空间进行特征的降维,并用距离判别、Fisher判别和logistic回归对降维后的结果进行了分类测试。实验结果表明:在核爆地震模式识别中,Isomap算法能在保持原数据绝大部分信息的前提下,很好地降低特征空间的维数,从而得到较高的正确识别率,并且优于PCA算法。

Feature selection is a very important and difficult problem in pattern recognition of underground nuclear explosions and natural earthquakes. To solve this problem, a new nonlinear dimensionality reduction method of Isomap is applied in this paper, and it is also compared with PCA method by experiments. The result shows that, Isomap effectively reduces the dimensions of the feature space and retains almost all of the information in the raw data （feature space）, and consequently obtains higher recognition rate than PCA.