Combinatorial individual differences scaling within the city-block metric

Research output: Contribution to journalArticle

Abstract

A new method is proposed for conducting individual differences scaling within the city-block metric that does not rely on gradient- or subgradient-based optimization. Instead, a combinatorial optimization scheme is utilized for identifying object coordinates minimizing the least-squares loss function. The illustrative application of combinatorial individual differences scaling within the city-block metric to schematic face stimuli suggests that the new method offers a promising alternative to gradient-based attempts for fitting city-block scaling models, which suffer from the well-documented difficulty of local minima.

Original languageEnglish (US)
Pages (from-to)931-946
Number of pages16
JournalComputational Statistics and Data Analysis
Volume51
Issue number2
DOIs
StatePublished - Nov 15 2006

Keywords

  • City-block metric
  • Combinatorial data analysis
  • Combinatorial optimization
  • INDSCAL model
  • Individual differences scaling
  • Iterative projection
  • Quadratic assignment
  • Uni-/multidimensional scaling

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Statistics, Probability and Uncertainty
  • Electrical and Electronic Engineering
  • Computational Mathematics
  • Numerical Analysis
  • Statistics and Probability

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