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 …

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 …

This paper presents a distance metric learning method for k-nearest neighbors regression.
We define the constraints based on triplets, which are built from the neighborhood of each …

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 has motivated a great deal of research over the last years due to its
robustness for many pattern recognition problems. In this paper, we develop a supervised …

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 …

Metric learning is an important problem in machine learning and pattern recognition. In this
paper, we propose a framework for metric learning based on information geometry. The key …

Distance metric learning aims to deal with the data distribution by learning a suitable
distance metric from the training instances. For distance metric learning, the optimization …

Distance metric learning is a powerful tool to improve performance in classification,
clustering and regression tasks. Many techniques have been proposed for distance metric …

Learning a distance metric from training samples is often a crucial step in machine learning
and pattern recognition. Locality, compactness and consistency are considered as the key …