A Practical Sequential Method for Principal Component Analysis
Neural Processing Letters
Image Compression by Approximated 2D Karhunen Loeve Transform
CAIP '99 Proceedings of the 8th International Conference on Computer Analysis of Images and Patterns
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Pattern Recognition Letters
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Neural Networks - 2003 Special issue: Advances in neural networks research — IJCNN'03
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Neural Computation
Theoretical Computer Science
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Digital Signal Processing
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Computers & Mathematics with Applications
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Intelligent Data Analysis
Concise Coupled Neural Network Algorithm for Principal Component Analysis
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
Novel Incremental Principal Component Analysis with Improved Performance
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
A New Incremental PCA Algorithm With Application to Visual Learning and Recognition
Neural Processing Letters
Extracting nonlinear features for multispectral images by FCMC and KPCA
Digital Signal Processing
A robust and globally convergent PCA learning algorithm
Control and Intelligent Systems
Multimodal medical image fusion using autoassociative neural network
Telehealth/AT '08 Proceedings of the IASTED International Conference on Telehealth/Assistive Technologies
Algorithms and networks for accelerated convergence of adaptive LDA
Pattern Recognition
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IEEE Transactions on Fuzzy Systems
Stability Analysis of Oja-RLS Learning Rule
Fundamenta Informaticae
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A new neural network-based approach is introduced for recursive computation of the principal components of a stationary vector stochastic process. The neurons of a single-layer network are sequentially trained using a recursive least squares squares (RLS) type algorithm to extract the principal components of the input process. The optimality criterion is based on retaining the maximum information contained in the input sequence so as to be able to reconstruct the network inputs from the corresponding outputs with minimum mean squared error. The proof of the convergence of the weight vectors to the principal eigenvectors is also established. A simulation example is given to show the accuracy and speed advantages of this algorithm in comparison with the existing methods. Finally, the application of this learning algorithm to image data reduction and filtering of images degraded by additive and/or multiplicative noise is considered