Multiblock Data Fusion in Statistics and Machine Learning. Tormod Næs

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      1.11 Abbreviations

In this book we will use a lot of abbreviations. Below follows a table with abbreviations used including the chapter(s) in which they appear. A small character ‘s’ in front of an abbreviation means ‘sparse’, e.g., sMB-PLS is the method sparse MB-PLS. For many methods mentioned below there are sparse versions; such as sPCA, sPLS, sSCA, sGCA, sMB-PLS and sMB-RDA. These are not mentioned explicitly in the table.

Table 1.2 Abbreviations of the different methods

Abbreviation	Full Description	Chapter
ACMTF	Advanced coupled matrix tensor factorisation	5
ASCA	ANOVA-simultaneous component analysis	6
BIBFA	Bayesian inter-battery factor analysis	9
DIABLO	Data integration analysis biomarker latent component omics	9
DI-PLS	Domain-invariant PLS	10
DISCO	Distinct and common components	5
ED-CMTF	Exponential dispersion CMTF	9
ESCA	Exponential family Simultaneous Component Analysis	5
GAS	Generalised association study	4,9
GAC	Generalised association coefficient	4
GCA	Generalised canonical analysis	2,5,7
GCD	General coefficient of determination	4
GCTF	Generalised coupled tensor factorisation	9
GFA	Group factor analysis	9
GPA	Generalised Procrustes analysis	9
GSCA	Generalised simultaneous component analysis	5
GSVD	Generalised singular value decomposition	9
IBFA	Inter-battery factor analysis	9
IDIOMIX	INDORT for mixed variables	9
INDORT	Individual differences scaling with orthogonal constraints	9
JIVE	Joint and individual variation explained	5
LiMM-PCA	Linear mixed model PCA	6
L-PLS	PLS regression for L-shaped data sets	8
MB-PLS	Multiblock partial least squares	7
MB-RDA	Multiblock redundancy analysis	10
MBMWCovR	Multiblock multiway covariates regression	10
MCR	Multivariate curve resolution	5,8
MFA	Multiple factor analysis	5
MOFA	Multi-omics factor analysis	9
OS	Optimal-scaling	2,5
PCA	Principal component analysis	2,5,8
PCovR	Principal covariates regression	2
PCR	Principal component regression	2
PESCA	Penalised ESCA	9
PE-ASCA	Penalised ASCA	6
PLS	Partial least squares	2
PO-PLS	Parallel and orthogonalised PLS regression	7
RDA	Redundancy analysis	7
RGCCA	Regularized generalized canonical correlation analysis	5
RM	Representation matrix approach	9
ROSA	Response oriented sequential alternation	7
SCA	Simultaneous component analysis	2,5
SLIDE	Structural learning and integrative decomposition	9
SMI	Similarity of matrices index	4
SO-PLS	Sequential and orthogonalised PLS regression	7,10 СКАЧАТЬ В начало < 18 19 20 21 22 23 24 > В конец