1) Canonical correlation analysis (CCA) is a statistical method that analyzes the correlation relationship between two sets of multidimensional variables.
2) CCA finds linear transformations of the two sets of variables so that their correlation is maximized. This can be formulated as a generalized eigenvalue problem.
3) The number of dimensions of the transformed variables is determined using Bartlett's test, which tests the eigenvalues against a chi-squared distribution.
1) Canonical correlation analysis (CCA) is a statistical method that analyzes the correlation relationship between two sets of multidimensional variables.
2) CCA finds linear transformations of the two sets of variables so that their correlation is maximized. This can be formulated as a generalized eigenvalue problem.
3) The number of dimensions of the transformed variables is determined using Bartlett's test, which tests the eigenvalues against a chi-squared distribution.
PFCC special lecture on materials informatics_nanotech2023Matlantis
At nano tech 2023, PFCC’s Rabi Shibata gave a special lecture on materials informatics.
[Lecture summary]
The growing interest in materials informatics (MI) has recently pushed Japanese companies into launching various MI projects, some of which have made successful achievements. At the same time, however, the resulting influx of MI-related information has caused confusion among those who are willing to get into MI.
In this lecture, PFCC’s Rabi Shibata gave an overview of the current MI landscape and where PFCC’s universal atomistic simulator Matlantis plays it’s role in the industry. He also introduced his own case study to illustrate what motivates materials scientists to take up MI.
https://matlantis.com/
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