A Fast Feature-based Dimension Reduction Algorithm for Kernel Classifiers
Neural Processing Letters
EURASIP Journal on Applied Signal Processing
Lie-group-type neural system learning by manifold retractions
Neural Networks
Comparison of Two Main Approaches to Joint SVD
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
On the convergence of ICA algorithms with symmetric orthogonalization
IEEE Transactions on Signal Processing
Blind channel estimation in orthogonally coded MIMO-OFDM systems: a semidefinite relaxation approach
IEEE Transactions on Signal Processing
General-rank beamforming for multi-antenna relaying schemes
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Optimal estimation and detection in homogeneous spaces
IEEE Transactions on Signal Processing
Joint receive-transmit beamforming for multi-antenna relaying schemes
IEEE Transactions on Signal Processing
Lp-Nested Symmetric Distributions
The Journal of Machine Learning Research
Global analysis of log likelihood criterion
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
An ICA learning algorithm utilizing geodesic approach
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Supervised subspace learning with multi-class lagrangian SVM on the grassmann manifold
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
Projection-like Retractions on Matrix Manifolds
SIAM Journal on Optimization
Note(s): On the consistency of coordinate-independent sparse estimation with BIC
Journal of Multivariate Analysis
Phase noise estimation and mitigation for cognitive OFDM systems
ICICA'12 Proceedings of the Third international conference on Information Computing and Applications
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Point-process principal components analysis via geometric optimization
Neural Computation
A Splitting Method for Orthogonality Constrained Problems
Journal of Scientific Computing
On the convergence of ICA algorithms with weighted orthogonal constraint
Digital Signal Processing
Hi-index | 35.70 |
This paper presents novel algorithms that iteratively converge to a local minimum of a real-valued function f (X) subject to the constraint that the columns of the complex-valued matrix X are mutually orthogonal and have unit norm. The algorithms are derived by reformulating the constrained optimization problem as an unconstrained one on a suitable manifold. This significantly reduces the dimensionality of the optimization problem. Pertinent features of the proposed framework are illustrated by using the framework to derive an algorithm for computing the eigenvector associated with either the largest or the smallest eigenvalue of a Hermitian matrix