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...
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| Online Access: |
Full text (MCPHS users only) |
|---|---|
| Main Author: | |
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Boca Raton :
CRC Press,
2014
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| Series: | Monographs on statistics and applied probability (Series) ;
129. |
| Subjects: | |
| Local Note: | ProQuest Ebook Central |
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.
