A number of clustering algorithms have been proposed for use in tasks where a limited degree of supervision is available. This prior knowledge is frequently provided in the form of pairwise must-link and cannot-link constraints. While the incorporation of pairwise supervision has the potential to improve clustering accuracy, the composition and cardinality of the constraint sets can significant...

Dimensionality reduction is a key data-analytic technique for mining high-dimensional data. In this paper, we consider a general problem of learning from pairwise constraints in the form of must-link and cannotlink. As one kind of side information, the must-link constraints imply that a pair of instances belongs to the same class, while the cannot-link constraints compel them to be different cl...

Conventional pairwise constrained metric learning methods usually restrict the distance between samples of a similar pair to be lower than a fixed upper bound, and the distance between samples of a dissimilar pair higher than a fixed lower bound. Such fixed bound based constraints, however, may not work well when the intraand inter-class variations are complex. In this paper, we propose a shrin...

Previous work on ordering events in text has typically focused on local pairwise decisions, ignoring globally inconsistent labels. However, temporal ordering is the type of domain in which global constraints should be relatively easy to represent and reason over. This paper presents a framework that informs local decisions with two types of implicit global constraints: transitivity (A before B ...

In multi-instance multi-label (MIML) learning, datasets are given in the form of bags, each of which contains multiple instances and is associated with multiple labels. This paper considers a novel instance clustering problem in MIML learning, where the bag labels are used as background knowledge to help group instances into clusters. The goal is to recover the class labels or to find the subcl...

A number of clustering algorithms have been proposed for use in tasks where a limited degree of supervision is available. This prior knowledge is frequently provided in the form of pairwise must-link and cannot-link constraints. While the incorporation of pairwise supervision has the potential to improve clustering accuracy, the composition and cardinality of the constraint sets can significant...

One of the most widely studied classes of constraints in constraint programming (CP) is that of table constraints. Numerous specialized filtering algorithms, enforcing the wellknown property called generalized arc consistency (GAC), have been developed for such constraints. Among the most successful GAC algorithms for table constraints, we find variants of simple tabular reduction (STR), like S...

This paper presents a novel symmetric graph regularization framework for pairwise constraint propagation. We first decompose the challenging problem of pairwise constraint propagation into a series of two-class label propagation subproblems and then deal with these subproblems by quadratic optimization with symmetric graph regularization. More importantly, we clearly show that pairwise constrai...