Support vector data description (SVDD) is a useful method for outlier detection and has been applied to a variety of applications. However, in the existing optimization procedure of SVDD, there are some issues which may lead to improper usage of SVDD. Some of the issues might already be known in practice, but the theoretical discussion, justification and correction are still lacking. Given the ...

In tradition probability statistics model, speaker verification threshold is instability in different test situations. A novel speaker verification method based on Support Vector Data Description (SVDD) is proposed to remedy the defect of probability statistics model. To simplify the threshold value setting and improve the robustness and recognition accuracy of the verification system, traditio...

Support vector data description (SVDD), proposed by [1], is a useful method for outlier detection. Its model is obtained by solving the dual optimization problem. In this paper, we point out some issues in their derivations. For example, they formulate SVDD as a non-convex problem and derive the dual problem only under some parameter settings. Given the wide use of SVDD, it is important to addr...

Support vector domain description (SVDD) is a well-known tool for pattern analysis when only positive examples are reliable. The SVDD model is often fitted by solving a quadratic programming problem, which is time consuming. This paper attempts to fit SVDD in the primal form directly. However, the primal objective function of SVDD is not differentiable which prevents the well-behaved gradient b...

In manufacturing industries, development of measurement leads to increase the number of monitoring variables and eventually the importance of multivariate control comes to the fore. Statistical process control (SPC) is one of the most widely used as multivariate control chart. Nevertheless, SPC is restricted to apply in processes because its assumption of data as following specific distribution...

Defect detection has been considered an efficient way to increase the yield rate of panels in thin film transistor liquid crystal display (TFT-LCD) manufacturing. In this study we focus on the array process since it is the first and key process in TFT-LCD manufacturing. Various defects occur in the array process, and some of them could cause great damage to the LCD panels. Thus, how to design a...

To enable post-processing, the output of a support vector data description (SVDD) should be a calibrated probability as done for SVM. Standard SVDD does not provide such probabilities. To create probabilities, we first generalize the SVDD model and propose two calibration functions. The first one uses a sigmoid model and the other one is based on a generalized extreme distribution model. To est...

This paper presents a novel Boundary-based approach in one-class classification that is inspired by support vector data description (SVDD). The SVDD is a popular kernel method which tries to fit a hypersphere around the target objects and of course more precise boundary is relied on selecting proper parameters for the kernel functions. Even with a flexible Gaussian kernel function, the SVDD cou...

This paper proposes the adaptation of Support Vector Data Description (SVDD) to the multiple kernel case (MK-SVDD), based on SimpleMKL. It also introduces a variant called Slim-MK-SVDD that is able to produce a tighter frontier around the data. For the sake of comparison, the equivalent methods are also developed for One-Class SVM, known to be very similar to SVDD for certain shapes of kernels....

Support Vector Data Description (SVDD) as a one-class classifier was developed to construct the minimum hypersphere that encloses all the data of the target class in a high dimensional feature space. However, SVDD treats the features of all data equivalently in constructing the minimum hypersphere since it adopts Euclidean distance metric and lacks the incorporation of prior knowledge. In this ...