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Title: A framework to combine vector-valued metrics into a scalar-metric: Application to data comparison (English)
Author: Piella, Gemma
Language: English
Journal: Applications of Mathematics
ISSN: 0862-7940 (print)
ISSN: 1572-9109 (online)
Volume: 68
Issue: 2
Year: 2023
Pages: 143-152
Summary lang: English
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Category: math
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Summary: 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. (English)
Keyword: generalized metric
Keyword: vector-valued metric
Keyword: scalarization
Keyword: image comparison
Keyword: structural similarity index
MSC: 46A40
MSC: 94A08
idZBL: Zbl 07675563
idMR: MR4574650
DOI: 10.21136/AM.2021.0090-21
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Date available: 2023-03-31T09:33:36Z
Last updated: 2023-09-13
Stable URL: http://hdl.handle.net/10338.dmlcz/151609
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