By Topic

Robust adaptive quasi-Newton algorithms for eigensubspace estimation

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $31
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Ouyang, S. ; Dept. of Commun. & Inf. Eng., Guilin Univ. of Electron. Technol., Guangxi, China ; Ching, P.C. ; Lee, T.

A novel quasi-Newton algorithm for adaptively estimating the principal eigensubspace of a covariance matrix by making use of an approximation of its Hessian matrix is derived. A rigorous analysis of the convergence properties of the algorithm by using the stochastic approximation theory is presented. It is shown that the recursive least squares (RLS) technique can be used to implement the quasi-Newton algorithm, which significantly reduces the computational requirements from O( pN2 ) to O( pN), where N is the data vector dimension and p is the number of desired eigenvectors. The algorithm is further generalised by introducing two adjustable parameters that efficiently accelerate the adaptation process. The proposed algorithm is applied to different applications such as eigenvector estimation and the Comon-Golub (1990) test in order to study the convergence behaviour of the algorithm when compared with others such as PASTd, NIC, and the Kang et al. (see IEEE Trans. Signal Process., vol. 48, p.3328-33, 2000) quasi-Newton algorithm. Simulation results show that the new algorithm is robust against changes of the input scenarios and is thus well suited to parallel implementation with online deflation

Published in:

Vision, Image and Signal Processing, IEE Proceedings -  (Volume:150 ,  Issue: 5 )