An Improved Deep Clustering Model for Underwater Acoustical Targets

Hand-craft features and clustering algorithms constitute the main parts of the unsupervised clustering system. Performance of the clustering deteriorates when the assumed probabilistic distribution of the data differs from the true one. This paper introduces a novel method that combines systematically the deep Boltzmann machine (DBM) with the Dirichlet process based Gaussian mixture model (DP-GMM) to bypass the problem of distribution mismatch. DBM is firstly used to extract the deep complex data features. By tactfully designing the distributions of different layers in DBM to make them compatible to that of the DP-GMM, we build a distribution consistent clustering system. The system is then jointly optimized by Markov chain Monte Carlo method with succinct updating formulations. The experimental results on two real databases of underwater acoustical target show the effectiveness and the robustness of the proposed clustering method.