Soft Modelling by Latent Variables: The Non-Linear Iterative Partial Least Squares (NIPALS) Approach

By H. Wold. 1975

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Citations in the corpus

Algorithms and applications for approximate nonnegative matrix factorization (2007, result): “Some experiments show that saddle point solutions can give reasonable results in the context of the problem, a finding confirmed by experiments with ALS-type algorithms in other contexts (de Leeuw et al., 1976; Gill et al., 1981; Smilde et al., 2004; Wold, 1966, 1975).”
PLS Path Modeling: From Foundations to Recent Developments and Open Issues for Model Assessment and Improvement (2010, methodology): “Other presentations of PLS Path Modeling given by Wold appeared in the same year (Wold 1975b, c).”
Sparsity and smoothness via the fused lasso (2005, methodology): “Many methods have been proposed for regularized or penalized regression, including ridge regression (Hoerl and Kennard, 1970), partial least squares (Wold, 1975) and principal components regression.”
On-line regression algorithms for learning mechanical models of robots: A survey (2011, methodology): “Moreover, like rfwr, the linear model approximation is performed efficiently using an incremental version of pls called nipals [117].”
Methodology and theory for partial least squares applied to functional data (2012, background): “See Wold (1975), Martens and Naes (1989), Helland (1990), Frank and Friedman (1993), Garthwaite (1994), Goutis and Fearn (1996), Durand and Sabatier (1997) and Nguyen and Rocke (2004).”
An Evaluation of PLS Based Complex Models: the Roles of Power Analysis, Predictive Relevance and GoF Index (2011, unknown): “The PLS approach to Structural Equation Models, also known as PLS Path Modeling (PLS-PM) is regarded as a component based SEM (Chin et al. 2003; Tenenhaus 2008) to model multiple causes and multiple indicators of a single latent variable, and to latent path models (Wold 1975, 1982, 1985).”
Prediction by Supervised Principal Components (2006, methodology): “PLS is one such approach, with a long history (Wold 1975; Frank and Friedman 1993; Hastie, Tibshirani, and Friedman 2001).”
Incremental Online Learning in High Dimensions (2005, methodology): “In Table 3, for R = N, that is, the same number of projections as the input dimensionality, the entire input space would be spanned by the projections ur and the regression results would be identical to that of ordinary linear regression (Wold, 1975).”
Partial least squares regression as an alternative to current regression methods used in ecology (2009, methodology): “The partial least squares regression The partial least squares regression (PLSR) was developed by Wold in the late 1960s for econometrics (Wold 1975) and then introduced as a tool to analyze data from chemical applications in the late 1970s (Geladi and Kowalski 1986, Martens et al. 1986, Mevik and…”