The nature of statistical learning theory
The nature of statistical learning theory
Outlier Detection Using Classifier Instability
SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Novelty detection: a review—part 1: statistical approaches
Signal Processing
Novelty detection: a review—part 2: neural network based 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
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
A Survey of Outlier Detection Methodologies
Artificial Intelligence Review
A Survey on Statistical Pattern Feature Extraction
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
An evaluation of dimension reduction techniques for one-class classification
Artificial Intelligence Review
Pattern Recognition
Robust feature extraction via information theoretic learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Variational Graph Embedding for Globally and Locally Consistent Feature Extraction
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
A Small Sphere and Large Margin Approach for Novelty Detection Using Training Data with Outliers
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Discriminant Analysis Based on Nonparametric Maximum Entropy
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
Feature extraction for one-class classification
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Information Theoretic Learning: Renyi's Entropy and Kernel Perspectives
Information Theoretic Learning: Renyi's Entropy and Kernel Perspectives
Feature extraction for novelty detection as applied to fault detection in machinery
Pattern Recognition Letters
A regularized correntropy framework for robust pattern recognition
Neural Computation
Prototype-Based Domain Description for One-Class Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Correntropy: Properties and Applications in Non-Gaussian Signal Processing
IEEE Transactions on Signal Processing
Generalized correlation function: definition, properties, and application to blind equalization
IEEE Transactions on Signal Processing - Part I
A Class of Single-Class Minimax Probability Machines for Novelty Detection
IEEE Transactions on Neural Networks
Robust Principal Component Analysis Based on Maximum Correntropy Criterion
IEEE Transactions on Image Processing
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In this paper, a novel feature extraction method based on regularized correntropy criterion (FEND-RCC) is proposed for novelty detection. In FEND-RCC, the presented criterion aims to maximize the difference between the correntropy of the normal data with their mean and the correntropy of the novel data with the mean of the normal data. Moreover, the optimal projection vectors in the objective function of FEND-RCC are iteratively obtained by the half-quadratic optimization technique. Experimental results on two synthetic data sets and thirteen benchmark data sets for novelty detection demonstrate that FEND-RCC is superior to its related approaches.