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 …

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

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 …

The need for appropriate ways to measure the distance or similarity between data is
ubiquitous in machine learning, pattern recognition and data mining, but handcrafting such …

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 …

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 …

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 …

Metric and kernel learning arise in several machine learning applications. However, most
existing metric learning algorithms are limited to learning metrics over low-dimensional data …

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 …

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 …