Research map / Learning theory and algorithms / Optimal transport
Quantifying Centrality for Complex Data
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Introduces centrality scores (C-scores) based on distance profiles to quantify centrality of random objects in general metric spaces, for identifying typical elements and constructing central regions.
Problem
Identifying central or typical elements in complex data taking values in general metric spaces, distinguishing them from atypical ones, and constructing central or normal regions at prespecified centrality levels.
Approach
Defines centrality scores (C-scores) from distance profiles of random objects in general metric spaces, using weighted distance-based constructions to quantify how central an object is.
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