Feedback recurrent neural network-based embedded vector and its application in topic model
作者: Lian-sheng LiSheng-jiang GanXiang-dong Yin
作者单位: 1School of Electronics and Information Engineering, Hunan University of Science and Engineering
2School of computer sciences Chengdu Normal University
刊名: EURASIP Journal on Embedded Systems, 2017, Vol.2017 (1), pp.1-6
来源数据库: Springer Journal
DOI: 10.1186/s13639-016-0038-6
关键词: Wireless sensor networksData aggregationAggregation treeAggregation delay
英文摘要: While mining topics in a document collection, in order to capture the relationships between words and further improve the effectiveness of discovered topics, this paper proposed a feedback recurrent neural network-based topic model. We represented each word as a one-hot vector and embedded each document into a low-dimensional vector space. During the process of document embedding, we applied the long short-term memory method to capture the backward relationships between words and proposed a feedback recurrent neural network to capture the forward relationships between words. In the topic model, we used the original and muted document pairs as positive samples and the original and random document pairs as negative samples to train the model. The experiments show that the proposed model...
全文获取路径: Springer  (合作)
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