文章基本信息

标题：Feedback recurrent neural network-based embedded vector and its application in topic model
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作者：Lian-sheng Li ; Sheng-jiang Gan ; Xiang-dong Yin 等
期刊名称：EURASIP Journal on Embedded Systems
印刷版ISSN：1687-3955
电子版ISSN：1687-3963
出版年度：2016
卷号：2017
期号：1
页码：1-6
DOI：10.1186/s13639-016-0038-6
出版社：Hindawi Publishing Corporation
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 consumes not only lower running time and memory but also has better effectiveness during topic analysis.
关键词：Wireless sensor networks;Data aggregation;Aggregation tree;Aggregation delay