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...
| Other Authors: | |
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| Format: | eBook |
| Language: | Inglés |
| Published: |
Boca Raton :
Chapman and Hall/CRC
2014.
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| Edition: | 1st edition |
| Series: | Monographs on statistics and applied probability (Series) ;
129. |
| Subjects: | |
| See on Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009628508006719 |
Table of Contents:
- 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
