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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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Constraint Neighborhood Projections for Semi-supervised Clustering
影响因子：19.118
DOI码：10.1109/TCYB.2013.2263383
所属单位：西南交通大学
发表刊物：IEEE Transactions on Cybernetics
刊物所在地：UNITED STATES
关键字：Clustering algorithms , Eigenvalues and eigenfunctions , Educational institutions , Clustering methods , Machine learning algorithms , Inference algorithms , Algorithm design and analysis
摘要：Semi-supervised clustering aims to incorporate the known prior knowledge into the clustering algorithm. Pairwise constraints and constraint projections are two popular techniques in semi-supervised clustering. However, both of them only consider the given constraints and do not consider the neighbors around the data points constrained by the constraints. This paper presents a new technique by utilizing the constrained pairwise data points and their neighbors, denoted as constraint neighborhood projections that requires fewer labeled data points (constraints) and can naturally deal with constraint conflicts. It includes two steps: 1) the constraint neighbors are chosen according to the pairwise constraints and a given radius so that the pairwise constraint relationships can be extended to their neighbors, and 2) the original data points are projected into a new low-dimensional space learned from the pairwise constraints and their neighbors. A CNP-Kmeans algorithm is developed based on the constraint neighborhood projections. Extensive experiments on University of California Irvine (UCI) datasets demonstrate the effectiveness of the proposed method. Our study also shows that constraint neighborhood projections (CNP) has some favorable features compared with the previous techniques.
合写作者：李天瑞, Yan Yang
第一作者：Hongjun Wang
论文类型：学术论文
通讯作者：Tao Li
论文编号：20141917692032
学科门类：工学
一级学科：计算机科学与技术
卷号：Volume: 44,
期号：Issue: 5
ISSN号：2168-2267
是否译文：否
发表时间：2014-01-09