Hostname: page-component-89b8bd64d-nlwjb Total loading time: 0 Render date: 2026-05-09T00:02:47.179Z Has data issue: false hasContentIssue false

Gradient algorithms for principal component analysis

Published online by Cambridge University Press:  17 February 2009

R. E. Mahony
Affiliation:
Dept of Systems Eng, Research School of Phys. Sciences and Eng, ANU, Canberra ACT 0200
U. Helmke
Affiliation:
Dept of Mathematics, University of Regensburg, 8400 Regensburg, F.R.G.
J. B. Moore
Affiliation:
Dept of Systems Eng, Research School of Phys. Sciences and Eng, ANU, Canberra ACT 0200
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the 'Save PDF' action button.

The problem of principal component analysis of a symmetric matrix (finding a p-dimensional eigenspace associated with the largest p eigenvalues) can be viewed as a smooth optimization problem on a homogeneous space. A solution in terms of the limiting value of a continuous-time dynamical system is presented. A discretization of the dynamical system is proposed that exploits the geometry of the homogeneous space. The relationship between the proposed algorithm and classical methods are investigated.

Information

Type
Research Article
Copyright
Copyright © Australian Mathematical Society 1996