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
Summary
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
