Communications of the ACM
The nature of statistical learning theory
The nature of statistical learning theory
Support vector domain description
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Constructing Boosting Algorithms from SVMs: An Application to One-Class Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Regularized principal manifolds
The Journal of Machine Learning Research
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Support Vector Data Description
Machine Learning
Estimating the Support of a High-Dimensional Distribution
Neural Computation
Neural Computation
Mercer kernel-based clustering in feature space
IEEE Transactions on Neural Networks
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In this paper, we investigate one-class and clustering problems by using statistical learning theory. To establish a universal framework, a unsupervised learning problem with predefined threshold η is formally described and the intuitive margin is introduced. Then, one-class and clustering problems are formulated as two specific η-unsupervised problems. By defining a specific hypothesis space in η-one-class problems, the crucial minimal sphere algorithm for regular one-class problems is proved to be a maximum margin algorithm. Furthermore, some new one-class and clustering marginal algorithms can be achieved in terms of different hypothesis spaces. Since the nature in SVMs is employed successfully, the proposed algorithms have robustness, flexibility and high performance. Since the parameters in SVMs are interpretable, our unsupervised learning framework is clear and natural. To verify the reasonability of our formulation, some synthetic and real experiments are conducted. They demonstrate that the proposed framework is not only of theoretical interest, but they also has a legitimate place in the family of practical unsupervised learning techniques.