Distance metric is a key issue in many machine learning algorithms. This paper considers a
general problem of learning from pairwise constraints in the form of must-links and cannot …

Many algorithms rely critically on being given a good metric over their inputs. For instance,
data can often be clustered in many “plausible” ways, and if a clustering algorithm such as K …

We address the problem of learning distance metrics using side-information in the form of
groups of" similar" points. We propose to use the RCA algorithm, which is a simple and …

Face identification is the problem of determining whether two face images depict the same
person or not. This is difficult due to variations in scale, pose, lighting, background …

Many learning algorithms use a metric defined over the input space as a principal tool, and
their performance critically depends on the quality of this metric. We address the problem of …

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 …

This paper introduces Pairwise Constrained Component Analysis (PCCA), a new algorithm
for learning distance metrics from sparse pairwise similarity/dissimilarity constraints in high …

The main theme of this paper is to develop a novel eigenvalue optimization framework for
learning a Mahalanobis metric. Within this context, we introduce a novel metric learning …

Classification problems abundantly arise in many computer vision tasks eing of supervised,
semi-supervised or unsupervised nature. Even when class labels are not available, a user …

Many computer vision tasks may be expressed as the problem of learning a mapping
between image space and a parameter space. For example, in human body pose …