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Constrained principal component analysis and related techniques /
In multivariate data analysis, regression techniques predict one set of variables from another while principal component analysis (PCA) finds a subspace of minimal dimensionality that captures the largest variability in the data. How can regression analysis and PCA be combined in a beneficial way? W...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Boca Raton :
CRC Press,
[2014]
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| Colección: | Monographs on statistics and applied probability (Series) ;
129. |
| Temas: | |
| Acceso en línea: | Texto completo |
| Sumario: | In multivariate data analysis, regression techniques predict one set of variables from another while principal component analysis (PCA) finds a subspace of minimal dimensionality that captures the largest variability in the data. How can regression analysis and PCA be combined in a beneficial way? Why and when is it a good idea to combine them? What kind of benefits are we getting from them? Addressing these questions, Constrained Principal Component Analysis and Related Techniques shows how constrained PCA (CPCA) offers a unified framework for these approaches. The book begins with four concre. |
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| Descripción Física: | 1 online resource (xvii, 224 pages .) |
| Bibliografía: | Includes bibliographical references and index. |
| ISBN: | 9781466556683 1466556684 |