منابع مشابه
Non-Metric Partial Least Squares
In this paper I review covariance-based Partial Least Squares (PLS) methods, focusing on common features of their respective algorithms and optimization criteria. I then show how these algorithms can be adjusted for use as optimal scaling tools. Three new PLS-type algorithms are proposed for the analysis of one, two or several blocks of variables: the NonMetric NIPALS, the Non-Metric PLS Regres...
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In this work we find out how PLS algorithms, properly adjusted, can work as optimal scaling algorithms. This new feature of PLS, which had until now been totally unexplored, allowed us to devise a new suite of PLS methods: the Non-Metric PLS (NM-PLS) methods. Mots-clès: Analyse des données data mining, Problèmes inverses et sparsité
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Partial least square (PLS) methods (also sometimes called projection to latent structures) relate the information present in two data tables that collect measurements on the same set of observations. PLS methods proceed by deriving latent variables which are (optimal) linear combinations of the variables of a data table. When the goal is to find the shared information between two tables, the ap...
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Number of latents The same number of factors will be extracted for PLS responses as for PLS factors. The researcher must specify how many latents to extract (in SPSS the default is 5). There is no one criterion for deciding how many latents to employ. Common alternatives are: 1. Cross-validating the model with increasing numbers of factors, then choosing the number with minimum prediction error...
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Partial least squares (PLS) was not originally designed as a tool for statistical discrimination. In spite of this, applied scientists routinely use PLS for classification and there is substantial empirical evidence to suggest that it performs well in that role. The interesting question is: why can a procedure that is principally designed for overdetermined regression problems locate and emphas...
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ژورنال
عنوان ژورنال: Electronic Journal of Statistics
سال: 2012
ISSN: 1935-7524
DOI: 10.1214/12-ejs724