A General Nonmetric Technique for Finding the Smallest Coordinate Space for a Configuration of Points

Let A 1, A 2,. . . , A n be any n objects, such as variables, categories, people, social groups, ideas, physical objects, or any other. The empirical data to be analyzed are coefficients of similarity or distance within pairs (A i, A i), such as correlation coefficients, conditional probabilities or likelihoods, psychological choice or confusion, etc. It is desired to represent these data parsimoniously in a coordinate space, by calculating m coordinates x ia for each A i for a semi-metric d of preassigned form d ij = d (| x i 1 - x j1 |, | x i 2 - x j 2 |,. . . , | x im - x jm |). The dimensionality m is sought to be as small as possible, yet satisfy the monotonicity condition that d ij < d kl whenever the observed data indicate that A i is “closer” to A j than A k is to A l. Minkowski and Euclidean spaces are special metric examples of d. A general coefficient of monotonicity μ is defined, whose maximization is equivalent to optimal satisfaction of the monotonicity condition, and which allows various options both for treatment of ties and for weighting error-of-fit. A general rationale for algorithm construction is derived for maximizing μ by gradient-guided iterations; this provides a unified mathematical solution to the basic operational problems of norming the gradient to assure proper convergence, of trading between speed and robustness against undesired stationary values, and of a rational first approximation. Distinction is made between single-phase (quadratic) and two-phase (bilinear) strategies for algorithm construction, and between “hard-squeeze” and “soft-squeeze” tactics within these strategies. Special reference is made to the rank-image and related transformational principles, as executed by current Guttman-Lingoes families of computer programs.