We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
A framework to combine vector-valued metrics into a scalar-metric: Application to data comparison.
- Authors
Piella, Gemma
- Abstract
Distance metrics are at the core of many processing and machine learning algorithms. In many contexts, it is useful to compute the distance between data using multiple criteria. This naturally leads to consider vector-valued metrics, in which the distance is no longer a real positive number but a vector. In this paper, we propose a principled way to combine several metrics into either a scalar-valued or vector-valued metric. We illustrate our framework by reformulating the popular structural similarity (SSIM) index and a simple case of the Wasserstein distance used for optimal transport.
- Subjects
MACHINE learning
- Publication
Applications of Mathematics, 2023, Vol 68, Issue 2, p143
- ISSN
0862-7940
- Publication type
Article
- DOI
10.21136/AM.2021.0090-21