Chapman and Hall/CRC
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? Wha
Introduction. Mathematical Foundation. Constrained Principal Component Analysis (CPCA). Special Cases and Related Methods. Related Topics of Interest. Different Constraints on Different Dimensions (DCDD). Epilogue. Appendix. Bibliography. Index.