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

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 …

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 …

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 …

We study the problem of learning local metrics for nearest neighbor classification. Most
previous works on local metric learning learn a number of local unrelated metrics. While …

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 …

Distance metric learning has been a hot research spot recently due to its high effectiveness
and efficiency in improving the performance of distance related methods, such as k nearest …

Similarity between objects plays an important role in both human cognitive processes and
artificial systems for recognition and categorization. How to appropriately measure such …