A general approach for similarity-based linear projections using a genetic algorithm

James A. Mouradian; Bernd Hamann; René Rosenbaum

doi:10.1117/12.909485

24 January 2012 A general approach for similarity-based linear projections using a genetic algorithm

James A. Mouradian, Bernd Hamann, René Rosenbaum

Author Affiliations +

Proceedings Volume 8294, Visualization and Data Analysis 2012; 82940L (2012) https://doi.org/10.1117/12.909485
Event: IS&T/SPIE Electronic Imaging, 2012, Burlingame, California, United States

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.

Citation Download Citation

James A. Mouradian, Bernd Hamann, and René Rosenbaum "A general approach for similarity-based linear projections using a genetic algorithm", Proc. SPIE 8294, Visualization and Data Analysis 2012, 82940L (24 January 2012); https://doi.org/10.1117/12.909485

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