1. School of Mechatronic Engineering and Automation, Foshan University, Foshan 528225 2. Research Center of Precision Sensing and Control, Institute of Automation, Chinese Academy of Sciences, Beijing 100190 3. Guangdong Province Smart City Infrastructure Health Monitoring and Evaluation Engineering Technology Research Center, Guangdong Engineering Technology Research Center, Foshan 528000
Abstract：Existing deep unsupervised clustering methods cannot make full use of the complementary information between the extracted features of different network structures due to the single network structure in them, and thus the clustering performance is restricted. A deep multi-network embedded clustering(DMNEC) algorithm is proposed to solve this problem. Firstly, the initialization parameters of each network are obtained by pretraining multi-network branches in an end-to-end manner. On this basis, the multi-network soft assignment is defined, then the clustering-oriented Kullback-Leibler divergence loss is established with the help of the multi-network auxiliary target distribution. The decoding network in the pretraining stage is finetuned via reconstruction loss to preserve the local structure and avoid the distortion of feature space. The weighted sum of reconstruction loss and clustering loss is optimized by stochastic gradient descent(SGD) and back propagation(BP) to jointly learn multi-network representation and cluster assignment. Experiments on four public image datasets demonstrate the superiority of the proposed algorithm.
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2021, Vol. 34 
