Nonlinear mapping technique for data visualization and clustering assessment of LIBS data: application to ChemCam data |
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Authors: | J Lasue R C Wiens T F Stepinski O Forni S M Clegg S Maurice ChemCam team |
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Institution: | (1) International Space and Response Division, Los Alamos National Laboratory, Los Alamos, NM 87544, USA;(2) Lunar and Planetary Institute, 3600 Bay Area Blvd, Houston, TX 77058, USA;(3) Department of Geography, University of Cincinnati, Cincinnati, OH 45221, USA;(4) IRAP, Univ. de Toulouse, CNRS, 14 Av. Ed. Belin, 31400 Toulouse, France;(5) Chemistry Division, Los Alamos National Laboratory, Los Alamos, NM 87544, USA |
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Abstract: | ChemCam is a remote laser-induced breakdown spectroscopy (LIBS) instrument that will arrive on Mars in 2012, on-board the
Mars Science Laboratory Rover. The LIBS technique is crucial to accurately identify samples and quantify elemental abundances
at various distances from the rover. In this study, we compare different linear and nonlinear multivariate techniques to visualize
and discriminate clusters in two dimensions (2D) from the data obtained with ChemCam. We have used principal components analysis
(PCA) and independent components analysis (ICA) for the linear tools and compared them with the nonlinear Sammon’s map projection
technique. We demonstrate that the Sammon’s map gives the best 2D representation of the data set, with optimization values
from 2.8% to 4.3% (0% is a perfect representation), together with an entropy value of 0.81 for the purity of the clustering
analysis. The linear 2D projections result in three (ICA) and five times (PCA) more stress, and their clustering purity is
more than twice higher with entropy values about 1.8. We show that the Sammon’s map algorithm is faster and gives a slightly
better representation of the data set if the initial conditions are taken from the ICA projection rather than the PCA projection.
We conclude that the nonlinear Sammon’s map projection is the best technique for combining data visualization and clustering
assessment of the ChemCam LIBS data in 2D. PCA and ICA projections on more dimensions would improve on these numbers at the
cost of the intuitive interpretation of the 2D projection by a human operator. |
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