Clustering can be improved with pairwise constraints that specify similarities between pairs of instances. However, randomly selecting constraints could lead to the waste of labeling effort, or even degrade the clustering performance. Consequently, how to actively select effective pairwise constraints to improve clustering becomes an important problem, which is the focus of this paper. In this ...

Dimensionality reduction is one of the key processes of high dimensional data analysis, including machine learning and pattern recognition. Constrained Locality Preserving Projections (CLPP) is a variant of Locality Preserving Projections (LPP) plus with pairwise constraints and constraints propagation. Like LPP, however, CLPP is still sensitive to noise and parameters. To overcome these proble...

the purpose of this paper is to introduce the concept of pairwise f-closedness in bitopological spaces. this space contains both of pairwise strongcompactness and pairwise s-closedness and contained in pairwise quasi h-closedness. the characteristics and relationships concerning this new class ofspaces with other corresponding types are established. moreover, several ofits basic and important p...

In this paper we present a multiview registration method for aligning range data. We first align scans pairwise with each other and use the pairwise alignments as constraints that the multiview step enforces while evenly diffusing the pairwise registration errors. This approach is especially suitable for registering large data sets, since using constraints from pairwise alignments does not requ...

Semi-supervised clustering under pairwise constraints (i.e. must-links and cannot-links) has been a hot topic in the data mining community in recent years. Since pairwise constraints provided by distinct domain experts may conflict with each other, a lot of research work has been conducted to evaluate the effects of noise imposing on semi-supervised clustering. In this paper, we introduce elite...

Distance metric has an important role in many machine learning algorithms. Recently, metric learning for semi-supervised algorithms has received much attention. For semi-supervised clustering, usually a set of pairwise similarity and dissimilarity constraints is provided as supervisory information. Until now, various metric learning methods utilizing pairwise constraints have been proposed. The...

Title of dissertation: Semi-supervised and Active Image Clustering with Pairwise Constraints from Humans Arijit Biswas, Doctor of Philosophy, 2014 Dissertation directed by: Prof. David W. Jacobs Department of Computer Science University of Maryland, College Park Clustering images has been an interesting problem for computer vision and machine learning researchers for many years. However as the ...

Semi-supervised clustering uses a small amount of supervised data to aid unsupervised learning. One typical approach specifies a limited number of must-link and cannotlink constraints between pairs of examples. This paper presents a pairwise constrained clustering framework and a new method for actively selecting informative pairwise constraints to get improved clustering performance. The clust...

Multi-view clustering has attracted much attention thanks to the capacity of multi-source information integration. Although numerous advanced methods have been proposed in past decades, most them generally overlook significance weakly-supervised and fail preserve feature properties multiple views, thus resulting unsatisfactory performance. To address these issues, this paper, we propose a novel...

Due to the demand for performance improvement and the existence of prior information, semi-supervised community detection with pairwise constraints becomes a hot topic. Most existing methods have been successfully encoding the must-link constraints, but neglect the opposite ones, i.e., the cannot-link constraints, which can force the exclusion between nodes. In this paper, we are interested in ...