Hydroacoustic Signal Classification Using Kernel Functions for Variable Feature Sets

Abstract

Large-scale geophysical monitoring systems raise the need for real-time feature extraction and signal classification. We study support vector machine (SVM) classification of hydroacoustic signals recorded by the Comprehensive Nuclear-Test-Ban Treaty's verification network. Due to constraints in the early signal processing most samples have incomplete feature sets with values missing not at random. We propose kernel functions explicitly incorporating Boolean representations of the missingness pattern through dedicated sub-kernels. For kernels with more than a few parameters, gradient-based model selection algorithms were employed. In the case of binary classification, an increase in classification accuracy as compared to baseline SVM and linear classifiers was observed. In the multi-class case we evaluated four different formulations of multi-class SVMs. Here, neither SVMs with standard nor with problem-specific kernels outperformed a baseline linear discriminant analysis.