Adaptive signal processing
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast RLS-Like Algorithm for Generalized Eigendecomposition and its Applications
Journal of VLSI Signal Processing Systems
Algorithms and networks for accelerated convergence of adaptive LDA
Pattern Recognition
On self-organizing algorithms and networks for class-separability features
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
In this paper, we introduced new adaptive learning algorithms to extract linear discriminant analysis (LDA) features from multidimensional data in order to reduce the data dimension space. For this purpose, new adaptive algorithms for the computation of the square root of the inverse covariance matrix Σ -1/2 are introduced. The proof for the convergence of the new adaptive algorithm is given by presenting the related cost function and discussing about its initial conditions. The new adaptive algorithms are used before an adaptive principal component analysis algorithm in order to construct an adaptive multivariate multi-class LDA algorithm. Adaptive nature of the new optimal feature extraction method makes it appropriate for on-line pattern recognition applications. Both adaptive algorithms in the proposed structure are trained simultaneously, using a stream of input data. Experimental results using synthetic and real multi-class multi-dimensional sequence of data, demonstrated the effectiveness of the new adaptive feature extraction algorithm.