Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Principal component neural networks: theory and applications
Principal component neural networks: theory and applications
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Mixtures of probabilistic principal component analyzers
Neural Computation
Support vector domain description
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Kernel PCA and de-noising in feature spaces
Proceedings of the 1998 conference on Advances in neural information processing systems II
Robust De-noising by Kernel PCA
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Kernel Whitening for One-Class Classification
SVM '02 Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines
Mustererkennung 1998, 20. DAGM-Symposium
Novelty detection: a review—part 1: statistical approaches
Signal Processing
Novelty detection: a review—part 2: neural network based approaches
Signal Processing
Estimating the Support of a High-Dimensional Distribution
Neural Computation
Approaches for automated detection and classification of masses in mammograms
Pattern Recognition
Supporting diagnosis of attention-deficit hyperactive disorder with novelty detection
Artificial Intelligence in Medicine
Expert Systems with Applications: An International Journal
Stacking for Ensembles of Local Experts in Metabonomic Applications
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Tree Covering within a Graph Kernel Framework for Shape Classification
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Reducing the run-time complexity of support vector data descriptions
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Combining SOM and local minimum enclosing spheres for novelty detection
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Fast support vector data descriptions for novelty detection
IEEE Transactions on Neural Networks
A Computable Plug-In Estimator of Minimum Volume Sets for Novelty Detection
Operations Research
Feature extraction for novelty detection as applied to fault detection in machinery
Pattern Recognition Letters
Front end analysis of speech recognition: a review
International Journal of Speech Technology
People re-identification by graph kernels methods
GbRPR'11 Proceedings of the 8th international conference on Graph-based representations in pattern recognition
A graph-kernel method for re-identification
ICIAR'11 Proceedings of the 8th international conference on Image analysis and recognition - Volume Part I
Computers and Industrial Engineering
Kernel PCA in application to leakage detection in drinking water distribution system
ICCCI'11 Proceedings of the Third international conference on Computational collective intelligence: technologies and applications - Volume Part I
PCA document reconstruction for email classification
Computational Statistics & Data Analysis
Novelty detection for the inspection of light-emitting diodes
Expert Systems with Applications: An International Journal
A Closed-form Solution for the Pre-image Problem in Kernel-based Machines
Journal of Signal Processing Systems
Geometrically local embedding in manifolds for dimension reduction
Pattern Recognition
On feature selection with principal component analysis for one-class SVM
Pattern Recognition Letters
Feature selection from high-order tensorial data via sparse decomposition
Pattern Recognition Letters
A meta-cognitive sequential learning algorithm for neuro-fuzzy inference system
Applied Soft Computing
L1 norm based KPCA for novelty detection
Pattern Recognition
Novelty detection using a new group outlier factor
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
Document categorization based on minimum loss of reconstruction information
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Computational Intelligence - Volume Part II
One-class classification with Gaussian processes
Pattern Recognition
Minimizer of the Reconstruction Error for multi-class document categorization
Expert Systems with Applications: An International Journal
Geometrical and computational aspects of Spectral Support Estimation for novelty detection
Pattern Recognition Letters
Review: A review of novelty detection
Signal Processing
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Kernel principal component analysis (kernel PCA) is a non-linear extension of PCA. This study introduces and investigates the use of kernel PCA for novelty detection. Training data are mapped into an infinite-dimensional feature space. In this space, kernel PCA extracts the principal components of the data distribution. The squared distance to the corresponding principal subspace is the measure for novelty. This new method demonstrated a competitive performance on two-dimensional synthetic distributions and on two real-world data sets: handwritten digits and breast-cancer cytology.