An Improved Deep Belief Network Prediction Model Based on Knowledge Transfer

Yue Zhang and Fangai Liu
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Yue Zhang: School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China
Fangai Liu: School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China

Future Internet, 2020, vol. 12, issue 11, 1-18

Abstract: A deep belief network (DBN) is a powerful generative model based on unlabeled data. However, it is difficult to quickly determine the best network structure and gradient dispersion in traditional DBN. This paper proposes an improved deep belief network (IDBN): first, the basic DBN structure is pre-trained and the learned weight parameters are fixed; secondly, the learned weight parameters are transferred to the new neuron and hidden layer through the method of knowledge transfer, thereby constructing the optimal network width and depth of DBN; finally, the top-down layer-by-layer partial least squares regression method is used to fine-tune the weight parameters obtained by the pre-training, which avoids the traditional fine-tuning problem based on the back-propagation algorithm. In order to verify the prediction performance of the model, this paper conducts benchmark experiments on the Movielens-20M (ML-20M) and Last.fm-1k (LFM-1k) public data sets. Compared with other traditional algorithms, IDBN is better than other fixed models in terms of prediction performance and training time. The proposed IDBN model has higher prediction accuracy and convergence speed.

Keywords: deep belief network; knowledge transfer; partial least squares regression; prediction (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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