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 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 …

In this paper, we introduce two novel metric learning algorithms, χ2-LMNN and GB-LMNN,
which are explicitly designed to be non-linear and easy-to-use. The two approaches achieve …

We introduce a method combining variational autoencoders (VAEs) and deep metric
learning to perform Bayesian optimisation (BO) over high-dimensional and structured input …

Abstract Domain adaptation (DA) has gained a lot of success in the recent years in computer
vision to deal with situations where the learning process has to transfer knowledge from a …

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 …

Distance metric is the basis of many learning algorithms, and its effectiveness usually has a
significant influence on the learning results. In general, measuring distance for numerical …

In this study, we develop a novel nonlinear metric learning method to improve biomarker
identification for Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI). Formulated …

The goal of transfer learning is to improve the performance of target learning task by
leveraging information (or transferring knowledge) from other related tasks. In this paper, we …