Overview and Recent Advances in Partial Least Squares

Overview and Recent Advances in Partial Least Squares

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 method...

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Bibliographische Detailangaben
Hauptverfasser: Rosipal, Roman, Krämer, Nicole
Format: Tagungsbericht
Sprache:eng
Schlagworte:
Applied sciences
Canonical Correlation Analysis
Computer science
control theory
systems
Data processing. List processing. Character string processing
Exact sciences and technology
Mathematics
Mean Square Error
Memory organisation. Data processing
Partial Little Square
Partial Little Square Regression
Principal Component Regression
Probability and statistics
Sciences and techniques of general use
Software
Statistics
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Beschreibung
Zusammenfassung: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].
ISSN:0302-9743
1611-3349
DOI:10.1007/11752790_2