A Tutorial on Distance Metric Learning: Mathematical Foundations, Algorithms and Experiments
Year: 2018
Type: preprint
Abstract: Distance metric learning is a branch of machine learning that aims to learndistances from the data. Distance metric learning can be useful to improve
similarity learning algorithms, and also has applications in dimensionality
reduction. This paper describes the distance metric learning problem and
analyzes its main mathematical foundations. In addition, it also discusses some
of the most popular distance metric learning techniques used in classification,
showing their goals and the required information to understand and use them.
Furthermore, some experiments to evaluate the performance of the different
algorithms are also provided. Finally, this paper discusses several
possibilities of future work in this topic.
Source: arXiv (Cornell University)
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2995882334