Many industrial processes are operated in multiple modes due to different manufacturing strategies. Multimodality of process data is often accompanied with nonlinear and non-Gaussian characteristics, which makes data-driven monitoring more complicated. In this paper, statistics pattern analysis (SPA) introduced extract low- high-order from raw data. Support vector description (SVDD), can deal p...

In previous research the support vector data description (SVDD) is proposed to solve the problem of one-class classification. In one-class classification, one set of data, called the target set, has to be distinguished from the rest of the feature space. In the original optimization of the support vector data description, two parameters have to be given beforehand by the user. In this paper a n...

Accurate urban land use/cover monitoring is an essential step towards a sustainable future. As a key part of the classification process, the characteristics of reference data can significantly affect classification accuracy and quality of producedmaps. However, ideal reference data is not always readily available; users frequently have difficulty generating sufficient reference data for some cl...

Support Vector Data Description (SVDD) has a limitation for dealing with a large data set in which computational load drastically increases as training data size becomes large. To handle this problem, we propose a new fast SVDDmethod using K-means clustering method. Our method uses divide-and-conquer strategy; trains each decomposed subproblems to get support vectors and retrains with the suppo...

In this paper, we investigate the problem of learning feature representation from unlabeled data using a single-layer K-means network. A K-means network maps the input data into a feature representation by finding the nearest centroid for each input point, which has attracted researchers’ great attention recently due to its simplicity, effectiveness, and scalability. However, one drawback of th...

In order to improve the identification accuracy of the high voltage circuit breakers’ (HVCBs) mechanical fault types without training samples, a novel mechanical fault diagnosis method of HVCBs using a hybrid classifier constructed with Support Vector Data Description (SVDD) and fuzzy c-means (FCM) clustering method based on Local Mean Decomposition (LMD) and time segmentation energy entropy (T...

This paper introduces a new method for data domain description , inspired by the Support Vector Machine by V.Vapnik, called the Support Vector Domain Description SVDD. This method computes a sphere shaped decision boundary with minimal volume around a set of objects. This data description can be used for novelty or outlier detection. It contains support vectors describing the sphere boundary an...

This article studies remote sensing image retrieval using kernel-based support vector data description (SVDD). We exploit deep SVDD, which is a well-known method for one-class classification to recover the most relevant samples from archive. To this end, neural network (DNN) jointly trained map into hypersphere of minimum volume in latent space. It expected that similar query are compressed ins...

We investigate anomaly detection in an unsupervised framework and introduce Long Short Term Memory (LSTM) neural network based algorithms. In particular, given variable length data sequences, we first pass these sequences through our LSTM based structure and obtain fixed length sequences. We then find a decision function for our anomaly detectors based on the One Class Support Vector Machines (...

This paper formulates the problem of distinguishing changed from unchanged pixels in remote sensing images as a minimum enclosing ball (MEB) problem with changed pixels as target class. The definition of the sphere shaped decision boundary with minimal volume that embraces changed pixels is approached in the context the support vector formalism adopting a support vector domain description (SVDD...