Adaptive algorithms for first principal eigenvector computation
Neural Networks
A power-based adaptive method for eigenanalysis without square-root operations
Digital Signal Processing
High-resolution source localization algorithm based on the conjugate gradient
EURASIP Journal on Advances in Signal Processing
Computing several eigenpairs of Hermitian problems by conjugate gradient iterations
Journal of Computational Physics
Minimum Cross Correlation Spreading Codes
Wireless Personal Communications: An International Journal
Parallel image processing with the block data parallel architecture
IBM Journal of Research and Development
A robust and globally convergent PCA learning algorithm
Control and Intelligent Systems
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Minimum Correlation Spreading Codes Design
Wireless Personal Communications: An International Journal
| Hi-index | 35.69 |
A conjugate gradient iteration is derived that converges to the set of r dominant/subdominant eigenpairs. This iteration is used to construct two eigenstructure tracking algorithms that track the r-dimensional dominant or subdominant subspaces of time-varying data or data-covariance matrices. The two eigenstructure tracking algorithms have update complexities O(m2r) and the other O(mr2), where m is the data dimension. The algorithms are customized to solve high resolution temporal and spatial frequency tracking problems. They are compared with existing techniques by tying into published simulation based performance tests. The algorithms demonstrate rapid convergence and tracking characteristics at a competitive cost