Multidimensional scaling and regression
نویسندگان
چکیده
Constrained multidimensional scaling was put on a firm theoretical basis by Jan De Leeuw and Willem Heiser in the 1980's. There is a simple method of fitting, based on distance via innerproducts, and a numerically more complicated one that is truly based on least-squares on distances. The unconstrained forms are known as principal coordinate analysis and nonmetric multidimensional scaling, respectively. Constraining the solution by external variables brings the power of classical regression analysis back into multidimensional data analysis. This idea is developed and illustrated, with emphasis on constrained principal coordinate analysis.
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تاریخ انتشار 1992