Binary classification based on SVDD projection and nearest neighbors

Support vector data description (SVDD) is a well-known kernel-based one-class classification method that exhibits intrinsic regularization ability and robustness versus low numbers of high-dimensional samples. However, the efficiency of SVDD is limited by the cubic time complexity. To solve this problem, this paper first investigates the effect of selecting a reduced subset as the training set of SVDD, while guaranteeing the classification quality. To this end, a new heuristic sample condensed rule, termed HSC, is proposed to accurately identify those potential support vectors that characterize the classification boundary. HSC can consider both the spatial distribution and local density features of training samples, and focus on selecting samples very close to the decision boundary. When dealing with the local density computation, we introduce the idea of K nearest neighbors (KNN) to examine the density of samples in the neighbors of the object to be classified. Finally, a condensed but informative subset obtained by HSC will be applied to train SVDD breezily. The experimental results show that HSC-based SVDD sensibly improves over conventional SVDD, in terms of the size of the training set while guaranteeing a comparable classification quality. In addition, it is competitive over other improved SVDD classifiers in terms of training and testing time.

Monitoring of solid-state fermentation of wheat straw in a pilot scale using FT-NIR spectroscopy and support vector data description

Power electronic circuits (PECs) are prone to various failures, whose classification is of paramount importance. This paper presents a data-driven based fault diagnosis technique, which employs a support vector data description (SVDD) method to perform fault classification of PECs. In the presented method, fault signals (e.g. currents, voltages, etc.) are collected from accessible nodes of circuits, and then signal processing techniques (e.g. Fourier analysis, wavelet transform, etc.) are adopted to extract feature samples, which are subsequently used to perform offline machine learning. Finally, the SVDD classifier is used to implement fault classification task. However, in some cases, the conventional SVDD cannot achieve good classification performance, because this classifier may generate some so-called refusal areas (RAs), and in our design these RAs are resolved with the one-against-one support vector machine (SVM) classifier. The obtained experiment results from simulated and actual circuits demonstrate that the improved SVDD has a classification performance close to the conventional one-against-one SVM, and can be applied to fault classification of PECs in practice.

Support Vector Data Description (SVDD) as a kernel-based method constructs a minimum hypersphere so as to enclose all the data of the target class in the kernel mapping space. In this paper, it is found that the kernel matrix G of SVDD can always have the Singular Value Decomposition (SVD) and the corresponding kernel mapping space can be made up of a set of base vectors generated by SVD. In order to make the kernel mapping more flexible, we induce a parameter λ into the set of base vectors and thus propose a novel SVDD with fractional order kernel (named λ-SVDD). In doing so, we can expand the solution space for the optimized dual problem of the SVDD. The experimental results on both synthetic data set and some real data sets show that the proposed method can bring more accurate description for all the tested target cases than the conventional SVDD.

With the rapid development of science and technology, unmanned aerial vehicles (UAVs) gradually become the worldwide focus of science and technology. Not only the development and application but also the security of UAV is of great significance to modern society. Different from methods using radar, optical or acoustic sensors to detect UAV, this paper proposes a novel distance-based support vector data description (SVDD) algorithm using hash fingerprint as feature. This algorithm does not need large number of training samples and its computation complexity is low. Hash fingerprint is generated by extracting features of signal preamble waveforms. Distance-based SVDD algorithm is employed to efficiently detect and recognize low, slow, small unmanned aerial vehicles (LSSUAVs) using 2.4GHz frequency band.

Support vector data description (SVDD) is an effective algorithm for nonlinear process monitoring. However, the conventional SVDD method can't deal with the incipient faults well, which has the small fault amplitude and is easy to be overlapped by industrial noises. Aiming at this problem, an improved SVDD, called probability related SVDD in local variable field (LVPSVDD), is proposed to detect the incipient faults in nonlinear processes. Firstly, the method divides process variables into several local variable fields by hierarchical clustering, and the SVDD model of each variable field is built. Then the sliding window technology is applied to the SVDD distance statistics, and the probability distribution change in the sliding window is measured by Kullback Leibler divergence (KLD). For each local variable field, the corresponding probability related monitoring statistic is developed to replace the original distance statistic. Finally, the global monitoring statistic is obtained by integrating the monitoring results of all local variable fields with Bayesian inference strategy. The method is illustrated with simulation on the continuous stirred tank reactor (CSTR).

Solving one-class problem with outlier examples by SVM

Density weighted support vector data description

Anomaly detection using improved deep SVDD model with data structure preservation

For the problem of a large dataset, we need to select a subset to represent the original dataset. Many scholars do pattern selection from the problem of the kNN (k-nearest neighbors). The distribution of a pattern's neighbors is usually uneven. In this paper, we define a fixed neighborhood sphere. When the pattern locates near the boundary of the data distribution, there will be fewer neighbors in the neighborhood sphere and when the pattern locates within the data distribution, there will be more neighbors in the neighborhood sphere. According to gather the statistic of the neighbors in a fixed neighborhood sphere, we can find those patterns locating near the boundary of the data distribution. In SVDD (Support Vector Data Description), those patterns are locating near the boundary of the data distribution have more information. They are those patterns which would be support vectors. We can use FNSPS (fixed neighborhood sphere pattern selection) algorithm to select those patterns, which locate near the boundary of the data distribution. The experimental results show that the performance of the SVDD will not go bad. The time complexity of the naive identifying the neighbors in the fixed neighborhood sphere is O(n2). And the time complexity of the SVDD is O(n3). If we set a lower threshold, the FNSPS algorithm can also be used to remove the noise in the targets.

As one classical anomaly detection technology, support vector data description (SVDD) has been successfully applied to nonlinear chemical process monitoring. However, the basic SVDD model cannot achieve a satisfactory fault detection performance in the complicated cases because of its intrinsic shallow learning structure. Motivated by the deep learning theory, one improved SVDD method, called ensemble deep SVDD (EDeSVDD), is proposed in order to monitor the process faults more effectively. In the proposed method, a deep support vector data description (DeSVDD) framework is firstly constructed by introducing the deep feature extraction procedure. Different to the traditional SVDD with only one feature extraction layer, DeSVDD is designed with multi-layer feature extraction structure and optimized by minimizing the data-enclosing hypersphere with the regularization of the deep network weights. Further considering the problem that DeSVDD monitoring performance is easily affected by the model structure and the initial weight parameters, an ensemble DeSVDD (EDeSVDD) is presented by applying the ensemble learning strategy based on Bayesian inference. A series of DeSVDD sub-models are generated at the parameter level and the structure level, respectively. These two levels of sub-models are integrated for a holistic monitoring model. To identify the cause variables for the detected faults, a fault isolation scheme is designed by applying the distance correlation coefficients to measure the nonlinear dependency between the original variables and the holistic monitoring index. The applications to the Tennessee Eastman process demonstrate that the proposed EDeSVDD model outperforms the traditional SVDD model and the DeSVDD model in terms of fault detection performance and can identify the fault cause variables effectively.

Illumination change is one of most important and difficult problems which prevent from applying face recognition to real applications. For solving this, we propose a method to compensate for different illumination conditions based on SVDD(Support Vector Data Description). In the proposed method, we first consider the SVDD training for the data belonging to the facial images under various illuminations, and model the data region for each illumination as the ball resulting from the SVDD training. Next, we compensate for illumination changes using feature vector projection onto the decision boundary of the SVDD ball. Finally, we obtain the pre-image under the identical illumination with input image. By repeated for each person, we can recognize a person with facial images under same illumination. We also perform the face recognition in order to verify the efficacy of proposed method.

Fault signal is always a problem that interferes with the operation of fiber board gluing control system. This paper proposes a Possibilistic C-Means (PCM) improved support vector data description (SVDD) method to construct a multi-classifier for the fault diagnosis of fiber gluing control system. In the proposed method, SVDD is employed to construct a multi-classifier for the fault diagnosis in the gluing system. Because the original SVDD is sensitive to the noises and outliers, we use PCM to improve the accuracy of classifier and then conduct a series of experiments. The results of the experiments show that the method proposed can identify the actual situation accurately and provide a high identification rate up to 97%, which is better than original SVDD.

Information entropy based sample reduction for support vector data description

The SVDD (support vector data description) is one of the most well-known one-class support vector learning methods, in which one tries the strategy of utilizing balls defined on the feature space in order to distinguish a set of normal data from all other possible abnormal objects. The major concern of this paper is to extend the main idea of the SVDD for the problem of pattern de-noising. Combining the projection onto the spherical decision boundary resulting from the SVDD together with a solver for the pre-image problem, we propose a new method for pattern de-noising. In the proposed method, we first solve the SVDD for the training data, then for each noisy test pattern, perform de-noising by projecting its feature vector onto the decision boundary on the feature space, and finally find the location of the de-noised pattern by obtaining the pre-image of the projection. The applicability of the proposed method is illustrated via an example dealing with noisy handwritten digits.