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 …

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 success of popular algorithms such as k-means clustering or nearest neighbor searches
depend on the assumption that the underlying distance functions reflect domain-specific …

Distance metric learning is a branch of machine learning that aims to learn distances from
the data, which enhances the performance of similarity-based algorithms. This tutorial …

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 …

Many machine learning algorithms, such as K Nearest Neighbor (KNN), heavily rely on the
distance metric for the input data patterns. Distance Metric learning is to learn a distance …

In this paper, we examine the generalization error of regularized distance metric learning.
We show that with appropriate constraints, the generalization error of regularized distance …

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 …

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 …

Over the past decades, distance metric learning has attracted a lot of interest in machine
learning and related fields. In this work, we propose an optimization framework for distance …