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 …

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 …

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 …

We propose a new method for local metric learning based on a conical combination of
Mahalanobis metrics and pair-wise similarities between the data. Its formulation allows for …

Metric learning has become a widespreadly used tool in machine learning. To reduce
expensive costs brought in by increasing dimensionality, low-rank metric learning arises as …

This paper describes the discipline of distance metric learning, a branch of machine learning
that aims to learn distances from the data. Distance metric learning can be useful to improve …

Distance metric learning is a fundamental problem in data mining and knowledge discovery.
Many representative data mining algorithms, such as k k-nearest neighbor classifier …

Recent studies [1]–[5] have suggested using con-straints in the form of relative distance
comparisons to represent domain knowledge: d (a, b)< d (c, d) where d (·) is the distance …

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 …

Metric learning methods have been shown to perform well on different learning tasks. Many
of them rely on target neighborhood relationships that are computed in the original feature …