English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Distance metric learning is a branch of machine learning that aims to learndistances from the data, which enhances the performance of similarity-basedalgorithms. This tutorial provides a theoretical background and foundations onthis topic and a comprehensive experimental analysis of the most-knownalgorithms. We start by describing the distance metric learning problem and itsmain mathematical foundations, divided into three main blocks: convex analysis,matrix analysis and information theory. Then, we will describe a representativeset of the most popular distance metric learning methods used inclassification. All the algorithms studied in this paper will be evaluated withexhaustive testing in order to analyze their capabilities in standardclassification problems, particularly considering dimensionality reduction andkernelization. The results, verified by Bayesian statistical tests, highlight aset of outstanding algorithms. Finally, we will discuss several potentialfuture prospects and challenges in this field. This tutorial will serve as astarting point in the domain of distance metric learning from both atheoretical and practical perspective.

A Tutorial on Distance Metric Learning: Mathematical Foundations,  Algorithms, Experimental Analysis, Prospects and Challenges (with Appendices  on Mathematical Background and Detailed Algorithms Explanation)