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 …

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 …

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 …

We propose a modified version of the K-means algorithm to cluster data. The proposed
algorithm adopts a novel nonmetric distance measure based on the idea of" point …

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 …

We present a taxonomy for local distance functions where most existing algorithms can be
regarded as approximations of the geodesic distance defined by a metric tensor. We …