A new node selection method of Deep Belief Network based on similarity
- 10.2991/amcce-17.2017.112How to use a DOI?
- deep belief network; node selection; similarity; deep learning; unsupervised learning
Research on the node selection problem in Deep Belief Network(DBN). A new node selection model based on similarity is proposed to select nodes in the pre-training process of DBN, which is an unsupervised process. Firstly, extracting the nodes feature from pre-training weight matrix, and then comparing them each other to get the similarity matrix, and using the similarity matrix to select nodes and reusing the nodes. Finally,the test on MINIST data set proves that the new model has better performance.
- © 2017, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Leifang Yan AU - MingYan Jiang PY - 2017/03 DA - 2017/03 TI - A new node selection method of Deep Belief Network based on similarity BT - Proceedings of the 2017 2nd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2017) PB - Atlantis Press SP - 645 EP - 649 SN - 2352-5401 UR - https://doi.org/10.2991/amcce-17.2017.112 DO - 10.2991/amcce-17.2017.112 ID - Yan2017/03 ER -