基于MPETM的城市功能区挖掘新方法

2018 15th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) Pub Date : 2018-12-01 DOI:10.1109/ICCWAMTIP.2018.8632580

Fan Mou, Yong He, Jing Peng, Yi Ma, Ze-zhong Zheng, Shengli Wang, Jiang Li

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引用次数: 0

摘要

城市功能区挖掘是智慧城市的重要组成部分。目前，处理这一问题的主要方法是概率主题模型。一些研究人员也使用深度学习方法来解决这个问题。本文提出了一种结合深度学习和概率主题模型的城市功能区挖掘新方法MPETM。实验结果表明，MPETM框架比目前流行的其他方法具有更高的精度。

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A New Urban Functional Regions Minig Method with MPETM

Urban functional regions mining plays an important role in Smart Cities. At present, the main approaches to deal with this problem are probabilistic topic models. Some researchers also use deep learning methods to solve this problem. In this paper, we present a new method for urban functional regions mining called MPETM which combines deep learning and probabilistic topic models. Experiment results show that the MPETM framework has superior accuracy than other current popular methods.

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2018 15th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)

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