title: Pairwise constrained concept factorization for data representation.
abstract: Concept factorization (CF) is a variant of non-negative matrix factorization (NMF). In CF, each concept is represented by a linear combination of data points, and each data point is represented by a linear combination of concepts. More specifically, each concept is represented by more than one data point with different weights, and each data point carries various weights called membership to represent their degrees belonging to that concept. However, CF is actually an unsupervised method without making use of prior information of the data. In this paper, we propose a novel semi-supervised concept factorization method, called Pairwise Constrained Concept Factorization (PCCF), which incorporates pairwise constraints into the CF framework. We expect that data points which have pairwise must-link constraints should have the same class label as much as possible, while data points with pairwise cannot-link constraints will have different class labels as much as possible. Due to the incorporation of the pairwise constraints, the learning quality of the CF has been significantly enhanced. Experimental results show the effectiveness of our proposed novel method in comparison to the state-of-the-art algorithms on several real world applications.
authors.affiliation: MOE-Microsoft Laboratory for Intelligent Computing and Intelligent Systems, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, PR China. Electronic address: h331076268@126.com.
authors.forename: Yangcheng
authors.initials: Y
authors.lastname: He
authors.affiliation: MOE-Microsoft Laboratory for Intelligent Computing and Intelligent Systems, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, PR China.
authors.forename: Hongtao
authors.initials: H
authors.lastname: Lu
authors.affiliation: MOE-Microsoft Laboratory for Intelligent Computing and Intelligent Systems, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, PR China.
authors.forename: Lei
authors.initials: L
authors.lastname: Huang
authors.affiliation: MOE-Microsoft Laboratory for Intelligent Computing and Intelligent Systems, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, PR China.
authors.forename: Saining
authors.initials: S
authors.lastname: Xie
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D057225:Data Mining; D016208:Databases, Factual; D005145:Face; D006801:Humans; D007091:Image Processing, Computer-Assisted; D016014:Linear Models; D010363:Pattern Recognition, Automated
keywords: Clustering; Concept factorization; Data representation; Pairwise constraints