Order-preserving embeddings: a geometric approach.
In Machine Learning, one typically has access to datasets which can be represented either as feature vectors for the items of interest or as pairwise distances or similarity scores between items. However, sometimes one has neither, but only an ordinal notion of similarity, where one might be able to...
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| Online Access: | http://hdl.handle.net/2047/D20316240 |