A general strategy for setting up supervised methods of multiblock data analysis

Autor:	Romain Glèlè Kakaï, El Mostafa Qannari, Essomanda Tchandao Mangamana
Rok vydání:	2021
Předmět:	Computer science Process Chemistry and Technology Latent variable Algorithm Spectroscopy Software Computer Science Applications Analytical Chemistry Block (data storage)
Zdroj:	Chemometrics and Intelligent Laboratory Systems. 217:104388
ISSN:	0169-7439
Popis:	A general strategy for setting up supervised methods of multiblock data analysis is outlined. This yields a unified framework where some already known methods are retreived and novel extensions are introduced. All the methods of analysis are based on the determination of latent variables associated with the various blocks of variables. They are derived from clear optimization criterion whose aim is to maximize either the sum of the covariances or the sum of squared covariances between the latent variable associated with the response variables and the block latent variables associated with the various explanatory datasets. New indices are proposed to help better interpreting the outcomes of the analyses. The methods are illustrated and compared based on simulated and real datasets.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::b5926e3a09c055c03794d8f5f9f88f92 https://doi.org/10.1016/j.chemolab.2021.104388 Zobrazit plný text záznamu Full Text from ScienceDirect