Advertisement

Canonical Correlation Analysis: Use of Composite Heliographs for Representing Multiple Patterns

  • Asaf Degani
  • Michael Shafto
  • Leonard Olson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4045)

Abstract

In a study of crew interaction with the automatic flight control system of the Boeing 757/767 aircraft, we observed 60 flights and recorded every change in the aircraft control modes, as well as every observable change in the operational environment. To quantify the relationships between the state of the operating environment and pilots’ actions and responses, we used canonical correlation because of its unique suitability for finding multiple patterns in large datasets. Traditionally, the results of canonical correlation analysis are presented by means of numerical tables, which are not conducive to recognizing multidimensional patterns in the data. We created a sun-ray-like diagram (which we call a heliograph) to present the multiple patterns that exist in the data by employing Alexander’s theory of centers.  The theory describes 15 heuristic properties that help create wholeness in a design, and can be extended to the problem of information abstraction and integration as well as packing of large amounts of data for visualization.

Keywords

Canonical Correlation Canonical Correlation Analysis Multiple Pattern Numerical Table Pitch Mode 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Alexander, C.: The Phenomenon of Life. The Center for Environmental Structure, Berkeley (2002)Google Scholar
  2. 2.
    Degani, A.: Modeling human-machine systems: On modes, error, and patterns of interaction. Unpublished doctoral dissertation. Georgia Institute of Technology, Atlanta, GA (1996) Google Scholar
  3. 3.
    Heymann, M., Degani, A.: Formal analysis and automatic generation of user interfaces: Approach, methodology, and an algorithm. Human Factors (paper accepted for publication)Google Scholar
  4. 4.
    Hotelling, H.: The most predictable criterion. Journal of Educational Psychology 26, 139–142 (1935)CrossRefGoogle Scholar
  5. 5.
    Shafto, M., Degani, A., Kirlik, A.: Canonical correlation analysis of data on human-automation interaction. In: Proceedings of the 41st Annual Meeting of the Human Factors and Ergonomics Society, Albuquerque, NM (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Asaf Degani
    • 1
  • Michael Shafto
    • 1
  • Leonard Olson
    • 1
  1. 1.NASA Ames Research CenterMountain ViewU.S.A

Personalised recommendations