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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 |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Boca Raton :
CRC Press,
[2014]
|
| Colección: | Monographs on statistics and applied probability (Series) ;
129. |
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 008 | 140604s2013 xxu fob 001 0 eng | ||
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| 100 | 1 | |a Takane, Yoshio, |e author. | |
| 245 | 1 | 0 | |a Constrained principal component analysis and related techniques / |c Yoshio Takane. |
| 264 | 1 | |a Boca Raton : |b CRC Press, |c [2014] | |
| 300 | |a 1 online resource (xvii, 224 pages .) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 1 | |a Monographs on statistics and applied probability ; |v 129 | |
| 588 | 0 | |a Print version record. | |
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a Front cover; Contents; List of figures; List of tables; Preface; About the author; Chapter 1: Introduction; Chapter 2: Mathematical foundation; Chapter 3: Constrained principal component analysis (CPCA); Chapter 4: Special cases and related methods; Chapter 5: Related topics of interest; Chapter 6: Different constraints on different dimensions (DCDD); Epilogue; Appendix; Bibliography; Back cover. | |
| 520 | |a 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. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Principal components analysis. | |
| 650 | 0 | |a Multivariate analysis. | |
| 650 | 2 | |a Multivariate Analysis | |
| 650 | 6 | |a Analyse en composantes principales. | |
| 650 | 6 | |a Analyse multivariée. | |
| 650 | 7 | |a MATHEMATICS |x Probability & Statistics |x General. |2 bisacsh | |
| 650 | 7 | |a Multivariate analysis |2 fast | |
| 650 | 7 | |a Principal components analysis |2 fast | |
| 758 | |i has work: |a Constrained principal component analysis and related techniques (Text) |1 https://id.oclc.org/worldcat/entity/E39PCFrGH4KVtkQRk4J8HyTMbm |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Takane, Yoshio. |t Constrained Principal Component Analysis and Related Techniques. |d Hoboken : Taylor and Francis, ©2013 |z 9781466556669 |
| 830 | 0 | |a Monographs on statistics and applied probability (Series) ; |v 129. | |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=1402688 |z Texto completo |
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