Distance-Based Partial Least Squares Analysis

  • Anjali Krishnan
  • Nikolaus Kriegeskorte
  • Hervé Abdi
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 56)

Abstract

Distances matrices are traditionally analyzed with statistical methods that represent distances as maps such as Metric Multidimensional Scaling (mds), Generalized Procrustes Analysis (gpa), Individual Differences Scaling (indscal), and distatis. Mds analyzes only one distance matrix at a time while gpa, indscal and distatis extract similarities between several distance matrices. However, none of these methods is predictive. Partial Least Squares Regression (plsr) predicts one matrix from another, but does not analyze distance matrices. We introduce a new statistical method called Distance-based Partial Least Squares Regression (displsr), which predicts one distance matrix from another. We illustrate displsr with data obtained from a neuroimaging experiment, which explored semantic categorization.

Key words

Partial least squares Regression Correlation Distance Mds Distatis 

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Anjali Krishnan
    • 1
  • Nikolaus Kriegeskorte
    • 2
  • Hervé Abdi
    • 3
  1. 1.Institute of Cognitive ScienceUniversity of Colorado BoulderBoulderUSA
  2. 2.MRC Cognition and Brain Sciences UnitUniversity of CambridgeCambridgeUK
  3. 3.School of Behavioral and Brain SciencesThe University of Texas at DallasRichardsonUSA

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