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Erschienen in:
2006 | OriginalPaper | Buchkapitel
Overview and Recent Advances in Partial Least Squares
verfasst von : Roman Rosipal, Nicole Krämer
Erschienen in: Subspace, Latent Structure and Feature Selection
Verlag: Springer Berlin Heidelberg
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Partial Least Squares (PLS) is a wide class of methods for modeling relations between sets of observed variables by means of latent variables. It comprises of regression and classification tasks as well as dimension reduction techniques and modeling tools. The underlying assumption of all PLS methods is that the observed data is generated by a system or process which is driven by a small number of latent (not directly observed or measured) variables. Projections of the observed data to its latent structure by means of PLS was developed by Herman Wold and coworkers [48,49,52].