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Entropy 2018, 20(4), 243; https://doi.org/10.3390/e20040243

Compression of a Deep Competitive Network Based on Mutual Information for Underwater Acoustic Targets Recognition

School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China
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Received: 24 January 2018 / Revised: 20 March 2018 / Accepted: 27 March 2018 / Published: 2 April 2018
(This article belongs to the Special Issue Information Theory in Machine Learning and Data Science)
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Abstract

The accuracy of underwater acoustic targets recognition via limited ship radiated noise can be improved by a deep neural network trained with a large number of unlabeled samples. However, redundant features learned by deep neural network have negative effects on recognition accuracy and efficiency. A compressed deep competitive network is proposed to learn and extract features from ship radiated noise. The core idea of the algorithm includes: (1) Competitive learning: By integrating competitive learning into the restricted Boltzmann machine learning algorithm, the hidden units could share the weights in each predefined group; (2) Network pruning: The pruning based on mutual information is deployed to remove the redundant parameters and further compress the network. Experiments based on real ship radiated noise show that the network can increase recognition accuracy with fewer informative features. The compressed deep competitive network can achieve a classification accuracy of 89.1 % , which is 5.3 % higher than deep competitive network and 13.1 % higher than the state-of-the-art signal processing feature extraction methods. View Full-Text
Keywords: underwater acoustic; ship radiated noise; mutual information; machine learning; deep learning; competitive learning underwater acoustic; ship radiated noise; mutual information; machine learning; deep learning; competitive learning
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Shen, S.; Yang, H.; Sheng, M. Compression of a Deep Competitive Network Based on Mutual Information for Underwater Acoustic Targets Recognition. Entropy 2018, 20, 243.

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