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 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 …

In this paper we propose a novel method for learning a Mahalanobis distance measure to be
used in the KNN classification algorithm. The algorithm directly maximizes a stochastic …

We present an algorithm for learning a quadratic Gaussian metric (Mahalanobis distance)
for use in classification tasks. Our method relies on the simple geometric intuition that a good …

Metric learning has been shown to significantly improve the accuracy of k-nearest neighbor
(kNN) classification. In problems involving thousands of features, distance learning …

In this paper, we raise important issues on scalability and the required degree of supervision
of existing Mahalanobis metric learning methods. Often rather tedious optimization …

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 …

The metric learning problem is concerned with learning a distance function tuned to a
particular task, and has been shown to be useful when used in conjunction with nearest …

In this paper, a supervised feature selection approach is presented, which is based on
metric applied on continuous and discrete data representations. This method builds a …

Metric and kernel learning arise in several machine learning applications. However, most
existing metric learning algorithms are limited to learning metrics over low-dimensional data …