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Spherical and Hyperbolic Embeddings of Data
2014
IEEE Transactions on Pattern Analysis and Machine Intelligence
Many computer vision and pattern recognition problems may be posed as the analysis of a set of dissimilarities between objects. For many types of data, these dissimilarities are not Euclidean (i.e. they do not represent the distances between points in a Euclidean space), and therefore cannot be isometrically embedded in a Euclidean space. Examples include shape-dissimilarities, graph distances and mesh geodesic distances. In this paper, we provide a means of embedding such non-Euclidean data
doi:10.1109/tpami.2014.2316836
pmid:26353065
fatcat:kb4gqcud3fcazmduukttjllmpm