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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Semi-Supervised Density Peaks Clustering Based on Constraint Projection
影响因子：2.259
DOI码：10.2991/ijcis.d.201102.002
所属单位：西南交通大学
发表刊物：International Journal of Computational Intelligence Systems
刊物所在地：FRANCE
关键字：Semi-supervised learning; Density peaks clustering; Pairwise constraint; Constraint projection
摘要：Clustering by fast searching and finding density peaks (DPC) method can rapidly identify the centers of clusters which have relatively high densities and high distances according to a decision graph. Various methods have been introduced to extend the DPC model over the past five years. DPC was originally presented as an unsupervised learning algorithm, and the thought of adding some prior information to DPC emerges as an alternative approach for improving its performance. It is extravagant to collect labeled data in real applications, and annotation of class labels is a nontrivial work, while pairwise constraint information is easier to get. Furthermore, the class label information can be converted into pairwise constraint information. Thus, we can take full advantage of pairwise constraints (or prior information) as much as possible. So this paper presents a new semi-supervised density peaks clustering algorithm (SSDPC) that uses constraint projection, which is flexible in loosening a few constraints over the learning stage. In the first stage, instances involving instance-level constraints and the remaining instances are concurrently projected to a lower dimensional data space led by the pairwise constraints, where viewing the distribution of data instances more clearly is available. Subsequently, traditional DPC is executed on the new lower dimensional dataset. Lastly, a few datasets from the Microsoft Research Asia Multimedia (MSRA-MM) image and UCI machine learning repository datasets are adopted in the experimental validation. The experimental results demonstrate that the proposed SSDPC achieves better performance than other three semi-supervised clustering algorithms.
合写作者：李天瑞, Jielei Chu, Jin Guo
第一作者：Shan Yan
论文类型：学术论文
通讯作者：Hongjun Wang
论文编号：20210809973353
学科门类：工学
一级学科：计算机科学与技术
卷号：14 - 1
期号：Issue 1
页面范围：140 - 147
ISSN号：1875-6883
是否译文：否
发表时间：2020-11-09