Metric learning has attracted a lot of interest over the last decade, but the generalization
ability of such methods has not been thoroughly studied. In this paper, we introduce an …

A good measure of similarity between data points is crucial to many tasks in machine
learning. Similarity and metric learning methods learn such measures automatically from …

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 …

Learning a proper distance metric is of vital importance for many distance based
applications. Distance metric learning aims to learn a set of latent factors based on which the …

Learning distance metrics from data is a fundamental problem in machine learning and
useful way to extract data-driven features by using the matrix root of a distance matrix …

For many data mining and machine learning tasks, the quality of a similarity measure is the
key for their performance. To automatically find a good similarity measure from datasets …

Distance metric learning aims to learn from the given training data a valid distance metric,
with which the similarity between data samples can be more effectively evaluated for …

Experts in disparate fields from biology to business are increasingly called upon to make
decisions based on data, but their background is not in data science, which is itself a …

The success of many computer vision problems and machine learning algorithms critically
depends on the quality of the chosen distance metrics or similarity functions. Due to the fact …

The Large Margin Nearest Neighbor (LMNN) metric learning algorithm has been
successfully used in many applications and continuously motivates new research works …