Feature Selection based on Principal Component Analysis for Underwater Source Localization by Deep Learning

TL;DR

This paper introduces an interpretable PCA-based feature selection method combined with semi-supervised learning for underwater source localization, improving accuracy and robustness with limited labeled data.

Contribution

It presents a novel PCA and PCR-based feature selection integrated into a semi-supervised framework for underwater source localization, enhancing efficiency and robustness.

Findings

95% faster training after feature selection

Improved robustness on depth variations

Effective with extremely limited labeled data

Abstract

In this paper, we propose an interpretable feature selection method based on principal component analysis (PCA) and principal component regression (PCR), which can extract important features for underwater source localization by only introducing the source location without other prior information. This feature selection method is combined with a two-step framework for underwater source localization based on the semi-supervised learning scheme. In the framework, the first step utilizes a convolutional autoencoder to extract the latent features from the whole available dataset. The second step performs source localization via an encoder multi-layer perceptron (MLP) trained on a limited labeled portion of the dataset. The proposed approach has been validated on the public dataset SwllEx-96 Event S5. The result shows the framework has appealing accuracy and robustness on the unseen data, especially when the number of data used to train gradually decreases. After feature selection, not only the training stage has a 95\% acceleration but the performance of the framework becomes more robust on the depth and more accurate when the number of labeled data used to train is extremely limited.