The Johnson-Lindenstrauss Lemma and the sphericity of some graphs
Journal of Combinatorial Theory Series A
Radial basis functions for multivariable interpolation: a review
Algorithms for approximation
Neural Networks
Approximate nearest neighbors: towards removing the curse of dimensionality
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Database-friendly random projections
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Regularized radial basis functional networks: theory and applications
Regularized radial basis functional networks: theory and applications
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
An elementary proof of a theorem of Johnson and Lindenstrauss
Random Structures & Algorithms
Using novelty detection to identify abnormalities caused by mean shifts in bivariate processes
Computers and Industrial Engineering
An Algorithmic Theory of Learning: Robust Concepts and Random Projection
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
Radial Basis Functions
Fault Diagnosis: Models, Artificial Intelligence, Applications
Fault Diagnosis: Models, Artificial Intelligence, Applications
Approximate nearest neighbors and the fast Johnson-Lindenstrauss transform
Proceedings of the thirty-eighth annual ACM symposium on Theory of computing
Nearest-neighbor-preserving embeddings
ACM Transactions on Algorithms (TALG)
Nonlinear Estimators and Tail Bounds for Dimension Reduction in l1 Using Cauchy Random Projections
The Journal of Machine Learning Research
Fast learning in networks of locally-tuned processing units
Neural Computation
A hybrid learning-based model for on-line detection and analysis of control chart patterns
Computers and Industrial Engineering
RBF nets in faults localization
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Neural Networks
Reformulated radial basis neural networks trained by gradient descent
IEEE Transactions on Neural Networks
Self-organizing mixture networks for probability density estimation
IEEE Transactions on Neural Networks
Density estimation and random variate generation using multilayer networks
IEEE Transactions on Neural Networks
Robust classifiers for data reduced via random projections
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
Multidimensional Systems and Signal Processing
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Principal component analysis (PCA) is a powerful fault detection and isolation method. However, the classical PCA, which is based on the estimation of the sample mean and covariance matrix of the data, is very sensitive to outliers in the training data ...