DYNAMIC MDS METHODS FOR THREE-WAY ASYMMETRIC DISSIMILARITY DATA

• YADOHISA Hiroshi

  Kagoshima University

• NIKI Naoto

  Science University of Tokyo

• HASHIGUCHI Hiroki

  Science University of Tokyo

Abstract

Two methods for analyzing three-way asymmetric (dis)similarity data are proposed. In both methods, asymmetry found in data is represented as a set of vectors, which is determined from the skew-symmetric parts in the matrices for visualizing the latent structure of the asymmetry. In the first method, an asymmetric matrix called "super asymmetric (dis)similarity matrix" is first composed from the given set of data matrices. Making the analysis proposed here of that matrix yields the estimated moving paths of the objects as well as the chronological changes of asymmetric vectors expressing the stress brought by the forced allocation of the points. In the second method, Procrustes transformation is used for analysis and the objects and the asymmetric vectors are represented together in each separate space. Graphical representation of the result in a definite time-space by smooth spline interpolation of trajectories is discussed with some numerical illustrations.

Journal

• Journal of the Japanese Society of Computational Statistics

  Journal of the Japanese Society of Computational Statistics 11 (1), 41-54, 1998-12-01

  Japanese Society of Computational Statistics

Keywords

• Multidimensional Scaling
• INDSCAL
• Dynamic MDS
• Asymmetry

Details

• CRID: 1573668926698156416
• NII Article ID: 110001235564
• NII Book ID: AA10823693
• ISSN: 09152350
• Text Lang: en
• Data Source:
  • CiNii Articles