Distance Metrics
Distance Metrics is a formal mathematical framework that quantifies the dissimilarity between data points in a geometric space by defining a specific function satisfying metric properties such as non-negativity, identity of indiscernibles, symmetry, and the triangle inequality.
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115.7K
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3.7K
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1.4K
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2017–2026
411
Variants
Distance Functions
1.6K
Distance Metric Learning with Application to Clustering with Side-Information
Eric P. Xing, Michael I. Jordan, Stuart Russell et al. · 2002 · 2.7K citations
On some results in fuzzy metric spaces
Aravind George, P. Veeramani · Fuzzy Sets and Systems · 1994 · 1.6K citations
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Stanford University
Stanford, United States
18 h-index
36 publications
4.6K citations
Carnegie Mellon University
Pittsburgh, United States
14 h-index
29 publications
4.6K citations
Tsinghua University
Beijing, China
14 h-index
34 publications
3.6K citations
New York University
New York, United States
14 h-index
23 publications
2.1K citations
University of California, Berkeley
Berkeley, United States
14 h-index
28 publications
14.4K citations
University of Michigan
Ann Arbor, United States
13 h-index
20 publications
1.8K citations
Texas A&M University
College Station, United States
12 h-index
28 publications
4K citations
Wuhan University
Wuhan, China
12 h-index
23 publications
1.8K citations
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Repository · 43 publications · 1.2K citations
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PROC · 18 publications · 705 citations
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