Support vector data description (SVDD) is a machine learning technique that is used for single-class classification and outlier detection. The idea of SVDD is to find a set of support vectors that defines a boundary around data. When dealing with online or large data, existing batch SVDD methods have to be rerun in each iteration. We propose an incremental learning algorithm for SVDD that uses ...

In image retrieval systems, images can be represented by single feature vectors or by clouds of points. A cloud of points offers a more flexible description but suffers from class overlap. We propose a novel approach for describing clouds of points based on support vector data description (SVDD). We show that combining SVDD-based classifiers improves the retrieval precision. We investigate the ...

One class classification is widely used in many applications. Only one target class is well characterized by instances in the training data in one class classification, and no instance is available for other non-target classes, or few instances are present and they cannot form statistically representative samples for the negative concept. A two-step paradigm employing nonnegative matrix factori...

Support Vector Data Description (SVDD) methods have been successfully applied to tasks such as hyperspectral anomaly detection [1] and spectral unmixing [2] [3]. Unfortunately, the performance of SVDD methods suffers when noisy or non-informative bands are present in the data. If a set of sparse bands could be identified for these techniques, the resulting data may improve SVDD performance whil...

Tracking moving objects in videos with quasi-stationary backgrounds is one of the most important and challenging tasks in video processing applications. In order to detect moving foreground regions in such videos the background and its changes should be modeled to help detecting moving regions of interest. Support Vector Data Descriptors (SVDD) can be employed in order to analytically model the...

Support vector data description (SVDD) provides a useful approach, with various practical applications, for constructing a description of multivariate data for single-class classification and outlier detection. The Gaussian kernel that is used in SVDD formulation allows a flexible data description defined by observations that are designated as support vectors. The data boundary of such a descri...

Learning good feature representation from unlabeled data has attracted researchers great attention recently. Among others, Kmeans clustering algorithm is popularly used to map the input data into a feature representation, by finding the nearest centroid for each input point. However, this ignores the density information of each cluster completely and the resulting representation may be too ters...

This paper developed an approach, the window-based validation set for support vector data description (WVS-SVDD), to determine optimal parameters for support vector data description (SVDD) model to map specific land cover by integrating training and window-based validation sets. Compared to the conventional approach where the validation set included target and outlier pixels selected visually a...

Defect signal is always a problem that interferes with the operation of fibre board gluing control system. Complex problem analysis and solving are an important evolutionary direction of TRIZ. This research creates a defect diagnosis methodology by synthesizing the Theory of Inventive Problem Solving (TRIZ) and a Possibilistic C-Means (PCM) improved Support Vector Data Description (SVDD) method...