A power-based adaptive method for eigenanalysis without square-root operations
Digital Signal Processing
On the discrete-time dynamics of a class of self-stabilizing MCA extraction algorithms
IEEE Transactions on Neural Networks
A self-stabilizing MSA algorithm in high-dimension data stream
Neural Networks
Adaptive multiple minor directions extraction in parallel using a PCA neural network
Theoretical Computer Science
Multidimensional Systems and Signal Processing
Fast adaptive algorithms for minor component analysis using Householder transformation
Digital Signal Processing
Convergence analysis for feng's MCA neural network learning algorithm
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
Hi-index | 35.68 |
An information criterion for adaptively estimating multiple minor eigencomponents of a covariance matrix is proposed. It is proved that the proposed criterion has a unique global minimum at the minor subspace and that all other equilibrium points are saddle points. Based on the gradient search approach of the proposed information criterion, an adaptive algorithm called adaptive minor component extraction (AMEX) is developed. The proposed algorithm automatically performs the multiple minor component extraction in parallel without the inflation procedure. Similar to the adaptive lattice filter structure, the AMEX algorithm also has the flexibility wherein increasing the number of the desired minor component does not affect the previously extracted minor components. The AMEX algorithm has a highly modular structure and the various modules operate completely in parallel without any delay. Simulation results are given to demonstrate the effectiveness of the AMEX algorithm for both the minor component analysis (MCA) and the minor subspace analysis (MSA)