聚类分析
计算机科学
代表(政治)
数据挖掘
人工智能
缺少数据
面子(社会学概念)
对抗制
生成语法
生成模型
机器学习
模式识别(心理学)
社会科学
社会学
政治
政治学
法学
作者
Qianqian Wang,Zhengming Ding,Zhiqiang Tao,Quanxue Gao,Yun Fu
标识
DOI:10.1109/icdm.2018.00174
摘要
Multi-view clustering, as one of the most important methods to analyze multi-view data, has been widely used in many real-world applications. Most existing multi-view clustering methods perform well on the assumption that each sample appears in all views. Nevertheless, in real-world application, each view may well face the problem of the missing data due to noise, or malfunction. In this paper, a new consistent generative adversarial network is proposed for partial multi-view clustering. We learn a common low-dimensional representation, which can both generate the missing view data and capture a better common structure from partial multi-view data for clustering. Different from the most existing methods, we use the common representation encoded by one view to generate the missing data of the corresponding view by generative adversarial networks, then we use the encoder and clustering networks. This is intuitive and meaningful because encoding common representation and generating the missing data in our model will promote mutually. Experimental results on three different multi-view databases illustrate the superiority of the proposed method.
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