Journal
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
Volume 143, Issue -, Pages 49-57Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.chemolab.2015.02.016
Keywords
Exploratory Data Analysis; Software; Matlab; Principal Component Analysis; Partial Least Squares; Big Data
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The Multivariate Exploratory Data Analysis (MEDA) Toolbox in Matlab is a set of multivariate analysis tools for the exploration of data sets. In the MEDA Toolbox, traditional exploratory plots based on Principal Component Analysis (PCA) or Partial Least Squares (PIS), such as score, loading and residual plots, are combined with new methods like MEDA, MEDA and SVI plots. The latter are aimed at solving some of the limitations found in the former to adequately extract conclusions from a data set. Also, other useful tools such as cross-validation algorithms, Multivariate Statistical Process Control (MSPC) charts and data simulation/approximation algorithms (ADICOV) are included in the toolbox. Finally, most of the exploratory tools are extended for their use with very large data sets (Big Data), with unlimited number of observations. (C) 2015 Elsevier B.V. All rights reserved.
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