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Van Benthem, Mark

Publications and source records attributed to Van Benthem, Mark.

Multivariate Curve Resolution (MCR) using Principal Components Inputs and Rigorous Equality and Inequality Constraints

This MATLAB pseudocode perform multivariate curve resolution (MCR) using PCA scores & loadings of data as inputs. It employs rigorous least squares equality and inequality constraints for all elements in the solution factor matrices. It also can be used to perform nonnegative matrix factorization. SAND2020-12650 M Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525.

Van Benthem, Mark↗

PARAFAC_T1

This is a method of performing trilinear analysis on large data sets using a modification of the PARAFAC-ALS algorithm. It iteratively decomposes the data matrix into a core matrix and three loading matrices based on the Tucker1 model. The algorithm is particularly useful for data sets that are too large to upload into a computer?s main memory. While the performance advantage in utilizing our algorithm is dependent on the number of data elements and dimensions of the data array, we have seen a significant performance improvement over operating PARAFAC-ALS on the full data set. SAND2020-12649 M Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525.

Van Benthem, Mark↗

Global analysis peak fitting for chemical spectroscopy data

The present invention relates to methods for analyzing a chemical sample. For instance, the methods herein allow for global analysis of spectroscopy data in order to extract useful chemical properties from complicated multidimensional data. Such analysis can optionally employ data compression to further expedite computer-implemented computation. In particular, the methods herein provide global analysis of data matrices explained by both linear and non-linear terms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗