Support vector domain description
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Stability-based validation of clustering solutions
Neural Computation
Estimating the Support of a High-Dimensional Distribution
Neural Computation
A survey of kernel and spectral methods for clustering
Pattern Recognition
Possibilistic Clustering in Feature Space
WILF '07 Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory
Dealing with non-metric dissimilarities in fuzzy central clustering algorithms
International Journal of Approximate Reasoning
The possibilistic C-means algorithm: insights and recommendations
IEEE Transactions on Fuzzy Systems
Comments on “A possibilistic approach to clustering”
IEEE Transactions on Fuzzy Systems
A contribution to convergence theory of fuzzy c-means and derivatives
IEEE Transactions on Fuzzy Systems
Input space versus feature space in kernel-based methods
IEEE Transactions on Neural Networks
A least-squares approach to anomaly detection in static and sequential data
Pattern Recognition Letters
Review: A review of novelty detection
Signal Processing
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In this paper,we study a kernel extension of the classic possibilistic c-means. In the proposed extension, we implicitly map input patterns into a possibly high-dimensional space by means of positive semidefinite kernels. In this new space, we model the mapped data by means of the possibilistic clustering algorithm. We study in more detail the special case where we model the mapped data using a single cluster only, since it turns out to have many interesting properties. The modeled memberships in kernel-induced spaces yield a modeling of generic shapes in the input space. We analyze in detail the connections to one-class support vector machines and kernel density estimation, thus, suggesting that the proposed algorithm can be used in many scenarios of unsupervised learning. In the experimental part, we analyze the stability and the accuracy of the proposed algorithm on some synthetic and real datasets. The results show high stability and good performances in terms of accuracy.