Abstract Support Vector Data Description (SVDD) is a popular one-class classifier for anomaly and novelty detection. But despite its effectiveness, SVDD does not scale well with data size. To avoid prohibitive training times, sampling methods select small subsets of the on which trains decision boundary hopefully equivalent to one obtained full set. According literature, good sample should ther...

Remote sensing is a major source of land cover information. Commonly, interest focuses on a single land cover class. Although a conventional multi-class classifier may be used to provide a map depicting the class of interest the analysis is not focused on that class and may be sub-optimal in terms of the accuracy of its classification. With a conventional classifier, considerable effort is dire...

A flexible description of images is offered by a cloud of points in a feature space. In the context of image retrieval such clouds can be represented in a number of ways. Two approaches are here considered. The first approach is based on the assumption of a normal distribution, hence homogeneous clouds, while the second one focuses on the boundary description, which is more suitable for multimo...

In one-class classification, one class of data, called the target class, has to be distinguished from the rest of the feature space. It is assumed that only examples of the target class are available. This classifier has to be constructed such that objects not originating from the target set, by definition outlier objects, are not classified as target objects. In previous research the support v...

This paper proposes a method of automatic facial reconstruction from a facial image partially corrupted by noise or occlusion. There are two key features of this method; the one is the automatic extraction of the correspondences ∗ corresponding author Facial Image Reconstruction from a Corrupted Image by SVDD 1213 between the corrupted input face and reference face without additional manual tas...

Support vector domain description (SVDD) is a useful tool in data mining, used for analysing the within-class distribution of multi-class data and to ascertain membership of a class with known training distribution. An important property of the method is its inner-product based formulation, resulting in its applicability to reproductive kernel Hilbert spaces using the “kernel trick”. This pract...