期刊
NEURAL NETWORKS
卷 19, 期 6-7, 页码 889-899出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.neunet.2006.05.014
关键词
information visualization; manifold extraction; multi-dimensional scaling (MDS); nonlinear dimensionality reduction; non-linear projection; gene expression
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. (c) 2006 Elsevier Ltd. All rights reserved.
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