caa a22 4500 605447624 CHVBK 20210128100133.0 cr unu---uuuuu 210128e20150601xx s 000 0 eng 10.1007/s11042-013-1796-y doi (NATIONALLICENCE)springer-10.1007/s11042-013-1796-y Local similarity learning for pairwise constraint propagation [Elektronische Daten] [Zhenyong Fu, Zhiwu Lu, Horace Ip, Hongtao Lu, Yunyun Wang] Pairwise constraint propagation studies the problem of propagating the scarce pairwise constraints across the entire dataset. Effective propagation algorithms have previously been designed based on the graph-based semi-supervised learning framework. Therefore, these previous constraint propagation methods rely critically on a good similarity measure over the data points. Improper or noisy similarity measurements may dramatically degrade the performance of the constraint propagation algorithms. In this paper, we make attempt to exploit the available pairwise constraints to learn a new set of similarities, which are consistent with the supervisory information in the pairwise constraints, before propagating these initial constraints. Our method is a local learning algorithm. More specifically, we compute the similarities at each data point through simultaneously minimizing the local reconstruction error and local constraint error. The proposed method has been tested in the constrained clustering tasks on eight real-life datasets and then shown to achieve significant improvements with respect to the state of the arts. The Author(s), 2014 Constrained clustering nationallicence Pairwise constraint propagation nationallicence Similarity learning nationallicence Semi-supervised learning nationallicence Fu Zhenyong College of Computer, Nanjing University of Posts and Telecommunications, 210003, Nanjing, Jiangsu, China aut Lu Zhiwu Department of Computer Science, School of Information, Renmin University of China, Beijing, China aut Ip Horace Department of Computer Science, City University of Hong Kong, Hong Kong, Hong Kong aut Lu Hongtao Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China aut Wang Yunyun College of Computer, Nanjing University of Posts and Telecommunications, 210003, Nanjing, Jiangsu, China aut Multimedia Tools and Applications Springer US; http://www.springer-ny.com 74/11(2015-06-01), 3739-3758 1380-7501 74:11<3739 2015 74 11042 https://doi.org/10.1007/s11042-013-1796-y text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s11042-013-1796-y text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Fu Zhenyong College of Computer, Nanjing University of Posts and Telecommunications, 210003, Nanjing, Jiangsu, China aut NATIONALLICENCE 700 1- Lu Zhiwu Department of Computer Science, School of Information, Renmin University of China, Beijing, China aut NATIONALLICENCE 700 1- Ip Horace Department of Computer Science, City University of Hong Kong, Hong Kong, Hong Kong aut NATIONALLICENCE 700 1- Lu Hongtao Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China aut NATIONALLICENCE 700 1- Wang Yunyun College of Computer, Nanjing University of Posts and Telecommunications, 210003, Nanjing, Jiangsu, China aut NATIONALLICENCE 773 0- Multimedia Tools and Applications Springer US; http://www.springer-ny.com 74/11(2015-06-01), 3739-3758 1380-7501 74:11<3739 2015 74 11042