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Visualization of three-way and higher order data sets (Conference Presentation)
Author(s): Barry M. Wise

Paper Abstract

Data sets of order three or more are increasingly common in areas ranging from biomedical imaging to threat detection, and are output from a number of spectroscopy (e.g. NIR, Raman, Excitation Emission Fluorescence) and spectrometry (e.g. SIMS) methods. Various chemometrics methods can be used to reduce the dimensionality of these data sets, and the resulting compressed data can then be visualized. These methods include Principal Components Analysis (PCA), Multivariate Curve Resolution (MCR), and Maximal Autocorrelation Factors (MAF) as well as numerous data clustering methods (e.g. HCA, DBSCAN, KNN) and classification techniques (e.g. PLS-DA, SIMCA). These methods can also be combined with traditional image analysis techniques such as particle analysis. This talk gives examples of how up front chemometric modeling can be used to extract relevant information which can then be visualized in two and three dimensions, and in time.

Paper Details

Date Published: 4 March 2019
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Proc. SPIE 10883, Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XXVI, 108830F (4 March 2019); doi: 10.1117/12.2516224
Show Author Affiliations
Barry M. Wise, Eigenvector Research, Inc. (United States)


Published in SPIE Proceedings Vol. 10883:
Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XXVI
Thomas G. Brown; Tony Wilson, Editor(s)

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