Cross-correlation neural network models for the smallest singular component of general matrix
Signal Processing - Special issue on neural networks
Lanczos Vectors versus Singular Vectors for Effective Dimension Reduction
IEEE Transactions on Knowledge and Data Engineering
PCA-based spatially adaptive denoising of CFA images for single-sensor digital cameras
IEEE Transactions on Image Processing
Fault diagnosis method based on moving window PCA
CCDC'09 Proceedings of the 21st annual international conference on Chinese Control and Decision Conference
Adaptive kernel principal component analysis
Signal Processing
Generalized weighted rules for principal components tracking
IEEE Transactions on Signal Processing
Adaptive Principal component EXtraction (APEX) and applications
IEEE Transactions on Signal Processing
Algorithms for accelerated convergence of adaptive PCA
IEEE Transactions on Neural Networks
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Two new on-line algorithms for adaptive principal component analysis (APCA) are proposed and discussed in order to solve the problem of on-line industrial process monitoring in this paper. Both the algorithms have the capability of extracting principal component eigenvectors on-line in a fixed size sliding data window with high dimensional input data. The first algorithm is based on the steepest gradient descent approach, which updates the covariance matrix with deflation transformation and on-line iteration. Based on neural networks, the second algorithm constructs the input data sequence with an on-line iteration method and trains the neural network in every data frame. The convergence of the two algorithms is then analyzed and the simulations are given to illustrate the effectiveness of the two algorithms. At last, the applications of the two algorithms are discussed.