SCALE FREE REDUCED RANK IMAGE ANALYSIS,

Abstract

In the traditional Guttman-Harris type image analysis, a transformation is applied to the data matrix such that each column of the transformed data matrix is the best least squares estimate of the corresponding column of the data matrix from the remaining columns. The model is scale free. However, it assumes (1) that the correlation matrix is basic and (2) that the data matrix is free of measurement errors. In this paper a more generalized model is developed that does not require these two assumptions. Computational procedures are suggested. (Author)

Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1970
Accession Number
AD0708058

Entities

People

  • Paul Horst

Organizations

  • University of Washington

Tags

Fields of Study

  • Mathematics

Readers

  • Computational Modeling and Simulation
  • Linear Algebra

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms