Abstract
The partial derivatives of the squared error loss function for the metric unfolding problem have a unique geometry which can be exploited to produce unfolding methods with very desirable properties. This paper details a simple unidimensional unfolding method which uses the geometry of the partial derivatives to find conditional global minima; i.e., one set of points is held fixed and the global minimum is found for the other set. The two sets are then interchanged. The procedure is very robust. It converges to a minimum very quickly from a random or non-random starting configuration and is particularly useful for the analysis of large data sets with missing entries.
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This paper benefits from many conversations with and suggestions from Howard Rosenthal.
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Poole, K.T. Least squares metric, unidimensional unfolding. Psychometrika 49, 311–323 (1984). https://doi.org/10.1007/BF02306022
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DOI: https://doi.org/10.1007/BF02306022