Can some one please explain me what is the difference between one class SVM and SVDD(support vector data description)
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$\begingroup$ Is there experience around in which cases one of these models is superior? $\endgroup$– MaxSDec 20, 2018 at 6:16
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Support vector data description (SVDD) finds the smallest hypersphere that contains all samples, except for some outliers. One-class SVM (OC-SVM) separates the inliers from the outliers by finding a hyperplane of maximal distance from the origin.
If the kernel function has the property that $k(\mathbf{x}, \mathbf{x}) = 1 \quad \forall \mathbf{x} \in \mathbb{R}^d$, SVDD and OC-SVM learn identical decision functions. Many common kernels have this property, such as RBF, Laplacian and $\chi^2$.
SVDD and OC-SVM are also equivalent in the case that all samples lie on a hypersphere centered at the origin, and are are linearly separable from it.
See Lampert, C. H. (2009). Kernel methods in computer vision (Chapter 5) for more detailed descriptions of these models.
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$\begingroup$ if we had to solve the svdd as an optimization problem how can we generate the constraints $\endgroup$– ou2105Nov 8, 2017 at 8:32
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$\begingroup$ How do these two algos compare with XGBoost and other methods for classification problems? Let's say for something that is not medical classification, but something more simple like predictions in marketing? $\endgroup$– EdisonMar 10, 2022 at 12:30