Statistical models
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Partial least squares regression - Wikipedia, the free encyclopedia
Partial least squares regression (PLS regression) is a statistical method that bears some relation to principal components regression ; instead of finding hyperplanes of minimum variance between the response and independent variables, it finds a linear regression model by projecting the predicted variables and the observable variables to a new space.
Análisis de componentes principales - Wikipedia, la enciclopedia libre
ACP de una distribución normal multivariante centrada en (1,3) con desviación estándar 3 en la dirección aproximada (0,878, 0,478) y desviación estándar 1 en la dirección perpendicular a la anterior.Non-negative matrix factorization - Wikipedia, the free encyclopedia
NMF redirects here. For the bridge convention, see new minor forcing . Non-negative matrix factorization (NMF) is a group of algorithms in multivariate analysis and linear algebra where a matrix , , is factorized into (usually) two matrices, and :
In statistics , latent Dirichlet allocation (LDA) is a generative model that allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar.