Jarkko Venna, and Samuel Kaski. Local multidimensional scaling. Neural Networks, 19, pp 889--899, 2006.
(preprint pdf)
In a visualization task, every nonlinear projection method
needs to make a compromise between trustworthiness and continuity.
In a trustworthy projection the visualized proximities hold in the
original data as well, whereas a continuous projection visualizes
all proximities of the original data. We show experimentally that
one of the multidimensional scaling methods, curvilinear components
analysis, is good at maximizing trustworthiness. We then extend it
to focus on local proximities both in the input and output space,
and to explicitly make a user-tunable parameterized compromise between
trustworthiness and continuity. The new method compares favorably to
alternative nonlinear projection methods.
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