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Record Nr. |
UNINA9910799902203321 |
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Autore |
Takane Yoshio |
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Titolo |
Constrained principal component analysis and related techniques / / Yoshio Takane, Professor Emeritus, McGill University Montreal, Quebec, Canada and Adjunct Professor at University of Victoria British Columbia, Canada |
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Pubbl/distr/stampa |
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Boca Raton : , : Chapman and Hall/CRC, , 2014 |
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©2014 |
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ISBN |
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0-429-18837-4 |
1-4665-5666-8 |
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Edizione |
[1st edition] |
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Descrizione fisica |
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1 online resource (244 p.) |
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Collana |
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Monographs on statistics and applied probability ; ; 129 |
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Classificazione |
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Disciplina |
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Soggetti |
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Principal components analysis |
Multivariate analysis |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references. |
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Nota di contenuto |
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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 |
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Sommario/riassunto |
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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 concrete examples of CPCA that provide readers with a basic understanding of the technique and its applications. It gives a detailed account of two key mathematical ideas in CPCA: projection and singular value decomposition. The author |
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