  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.
