We investigate multidimensional scaling with Bregman divergences and show that the Sammon mapping can be thought of as a truncated Bregman multidimensional scaling (BMDS). We show that the full BMDS improves upon the Sammon mapping on some standard data sets and investigate the reasons underlying this improvement. We then introduce two families of BMDS which use opposite strategies to create good mappings of standard data sets and investigate these opposite strategies analytically.
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