Jolliffe I. Principal Component Analysis (2ed., Springer, 2002)(518s)

14.7. Concluding Remarks 405space orthogonal to the components found so far. Clearly, this techniqueloses the variance maximization property of PCA but, like the techniquesof Section 11.2, it can be thought of as an alternative that simplifies interpretation.In the present case simplification is in the direction of the user’sexpectations.14.7 Concluding RemarksIt has been seen in this book that PCA can be used in a wide variety of differentways. Many of the topics covered, especially in the last four chapters,are of recent origin and it is likely that there will be further advances inthe near future that will help to clarify the usefulness, in practice, of someof the newer techniques. Developments range from an increasing interest inmodel-based approaches on the one hand to the mainly algorithmic ideas ofneural networks on the other. Additional uses and adaptations of PCA arecertain to be proposed and, given the large number of fields of applicationin which PCA is employed, it is inevitable that there are already some usesand modifications of which the present author is unaware.In conclusion, it should be emphasized again that, far from being an oldand narrow technique, PCA is the subject of much recent research and hasgreat versatility, both in the ways in which it can be applied, and in thefields of application for which it is useful.