Local multidimensional scaling.

Jarkko Venna; Samuel Kaski

doi:10.1016/j.neunet.2006.05.014

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Local multidimensional scaling.

Jarkko Venna·Samuel Kaski

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Abstract

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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Algorithms Artificial Intelligence Cluster Analysis Computer Simulation Humans Models, Statistical Pattern Recognition, Automated Principal Component Analysis Comparative Study Journal Article Research Support, Non-U.S. Gov't

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Jarkko Venna, Samuel Kaski. 2006-06-19. Local multidimensional scaling.. https://doi.org/10.1016/j.neunet.2006.05.014

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