Multilayer feedforward networks are universal approximators
Neural Networks
Learning factorial codes by predictability minimization
Neural Computation
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
A Mathematical Theory of Communication
A Mathematical Theory of Communication
Neural Computation
Neural Computation
Augmenting Supervised Neural Classifier Training Using a Corpus of Unlabeled Data
KI '02 Proceedings of the 25th Annual German Conference on AI: Advances in Artificial Intelligence
Exploratory analysis of fMRI data by fuzzy clustering: philosophy, strategy, tactics, implementation
Exploratory analysis and data modeling in functional neuroimaging
Novelty detection: a review—part 1: statistical approaches
Signal Processing
Support Vector Data Description
Machine Learning
An Approach to Novelty Detection Applied to the Classification of Image Regions
IEEE Transactions on Knowledge and Data Engineering
A Survey of Outlier Detection Methodologies
Artificial Intelligence Review
A Theory for Learning by Weight Flow on Stiefel-Grassman Manifold
Neural Computation
Nonlinear Coordinate Unfolding Via Principal Curve Projections with Application to Nonlinear BSS
Neural Information Processing
Minimum spanning tree based one-class classifier
Neurocomputing
ACM Computing Surveys (CSUR)
Combining different biometric traits with one-class classification
Signal Processing
Initialisation of Nonlinearities for PNL and Wiener systems Inversion
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
A theoretical framework for multi-sphere support vector data description
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
ACS'06 Proceedings of the 6th WSEAS international conference on Applied computer science
Multiple distribution data description learning algorithm for novelty detection
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
Active selection of clustering constraints: a sequential approach
Pattern Recognition
Review: A review of novelty detection
Signal Processing
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According to Barlow (1989), feature extraction can be understood as finding a statistically independent representation of the probability distribution underlying the measured signals. The search for a statistically independent representation can be formulated by the criterion of minimal mutual information, which reduces to decorrelation in the case of gaussian distributions. If nongaussian distributions are to be considered, minimal mutual information is the appropriate generalization of decorrelation as used in linear Principal Component Analyses (PCA). We also generalize to nonlinear transformations by only demanding perfect transmission of information. This leads to a general class of nonlinear transformations, namely symplectic maps. Conservation of information allows us to consider only the statistics of single coordinates. The resulting factorial representation of the joint probability distribution gives a density estimation. We apply this concept to the real world problem of electrical motor fault detection treated as a novelty detection task.