硕士生导师
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学历：博士研究生毕业
学位：工学博士学位
办公地点：犀浦3号教学楼31529
毕业院校：四川大学
学科：电子信息. 软件工程. 计算机应用技术
所在单位：计算机与人工智能学院
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Deep Belief Networks Oriented Clustering
DOI码：10.1109/ISKE.2015.8
所属单位：西南交通大学
发表刊物：2015 10th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)
刊物所在地：Taipei, Taiwan
关键字：deep learning, unsupervised learning, deep belief network, clustering, fuzzy c-means
摘要：Deep learning has been popular for a few years, and it shows great capability on unsupervised leaning of representation. Deep belief network consists of multi layers of restricted Boltzmann machine(RBM) and a deep auto-encoder, which uses a stack architecture learning feature layer by layer. The learning rule is that one deeper layer learns more complex representations, which are the high level features of the input data, from the representations learnt by the layer before. Fuzzy C-Means(FCM) is one of the most popular clustering algorithms, which allows one piece of data belong to several clusters. In this paper the authors propose a novel clustering model, and introduce a novel clustering technique(DBNOC) which combines deep belief network and fuzzy c-means. The main idea is that: first, it clusters with the high level representations learnt by stacked RBM to produce the initial cluster center, then it uses the fine-tune step including one center holding clustering algorithm and deep auto-encoder to optimize the cluster center and membership between input data and every cluster by cross iteration. The authors use FCM clustering algorithm to fulfill the model and do experiment on both low dimensional datasets and high dimensional datasets. The experiment results suggest that the proposed deep belief network oriented clustering method is better than the standard K-Means and FCM algorithm on the test datasets. Even on high dimensional datasets, the DBNOC clustering method show more generalization. What's more, the proposed model is suitable both in theoretical and practical.
合写作者：李天瑞, Yan Yang
第一作者：Qi Yang
论文类型：学术论文
通讯作者：Hongjun Wang
论文编号：20162002387658
学科门类：工学
一级学科：计算机科学与技术
页面范围：58-65
ISSN号：978-1-4673-9323-2
是否译文：否
发表时间：2015-12-24