Machine Learning
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
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
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On Combining One-Class Classifiers for Image Database Retrieval
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
The Journal of Machine Learning Research
An Improved Cluster Labeling Method for Support Vector Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Novel Kernel Method for Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimating the Support of a High-Dimensional Distribution
Neural Computation
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Dynamic Characterization of Cluster Structures for Robust and Inductive Support Vector Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Domain described support vector classifier for multi-classification problems
Pattern Recognition
Neural Computation
A SVM-based cursive character recognizer
Pattern Recognition
Clustering Based on Gaussian Processes
Neural Computation
Constructing sparse kernel machines using attractors
IEEE Transactions on Neural Networks
Fast support-based clustering method for large-scale problems
Pattern Recognition
Dynamic Dissimilarity Measure for Support-Based Clustering
IEEE Transactions on Knowledge and Data Engineering
De-noising by soft-thresholding
IEEE Transactions on Information Theory
The pre-image problem in kernel methods
IEEE Transactions on Neural Networks
Equilibrium-Based Support Vector Machine for Semisupervised Classification
IEEE Transactions on Neural Networks
Forecasting trends of high-frequency KOSPI200 index data using learning classifiers
Expert Systems with Applications: An International Journal
Transductive Bayesian regression via manifold learning of prior data structure
Expert Systems with Applications: An International Journal
Forecasting nonnegative option price distributions using Bayesian kernel methods
Expert Systems with Applications: An International Journal
Sequential manifold learning for efficient churn prediction
Expert Systems with Applications: An International Journal
Probabilistic generative ranking method based on multi-support vector domain description
Information Sciences: an International Journal
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In this paper, we propose a novel pattern denoising method that utilizes the topological property of a support that describes the distribution of normal patterns to denoise noisy patterns. The method first trains a support function which captures the domain of normal patterns and then construct a so-called multi-basin system associated with the trained support function. By moving noisy patterns along the trajectories of the multi-basin system, noise is removed while the pattern recovers its normality. The denoised pattern is obtained when the noisy pattern arrives at the attracting manifold generated by a set of normal patterns and this is the most similar normal pattern with the noisy pattern in the topological sense. Through simulations on some toy dataset and real image datasets, we show that the proposed framework effectively removes the noise while preserving the information contained in the noisy pattern.