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Proceedings Paper

A general approach for similarity-based linear projections using a genetic algorithm
Author(s): James A. Mouradian; Bernd Hamann; René Rosenbaum
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Paper Abstract

A widely applicable approach to visualizing properties of high-dimensional data is to view the data as a linear projection into two- or three-dimensional space. However, developing an appropriate linear projection is often difficult. Information can be lost during the projection process, and many linear projection methods only apply to a narrow range of qualities the data may exhibit. We propose a general-purpose genetic algorithm to develop linear projections of high-dimensional data sets which preserve a specified quality of the data set as much as possible. The obtained results show that the algorithm converges quickly and reliably for a variety of different data sets.

Paper Details

Date Published: 25 January 2012
PDF: 12 pages
Proc. SPIE 8294, Visualization and Data Analysis 2012, 82940L (25 January 2012); doi: 10.1117/12.909485
Show Author Affiliations
James A. Mouradian, Univ. of California, Davis (United States)
Bernd Hamann, Univ. of California, Davis (United States)
René Rosenbaum, Univ. of California, Davis (United States)


Published in SPIE Proceedings Vol. 8294:
Visualization and Data Analysis 2012
Pak Chung Wong; David L. Kao; Ming C. Hao; Chaomei Chen; Robert Kosara; Mark A. Livingston; Jinah Park; Ian Roberts, Editor(s)

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