Structured one-class classification.

The one-class classification problem aims to distinguish a target class from outliers. The spherical one-class classifier (SOCC) solves this problem by finding a hypersphere with minimum volume that contains the target data while keeping outlier samples outside. SOCC achieves satisfactory performance only when the target samples have the same distribution tendency in all orientations. Therefore, the performance of the SOCC is limited in the way that many superfluous outliers might be mistakenly enclosed. The authors propose to exploit target data structures obtained via unsupervised methods such as agglomerative hierarchical clustering and use them in calculating a set of hyperellipsoidal separating boundaries. This method is named the structured one-class classifier (TOCC). The optimization problem in TOCC can be formulated as a series of second-order cone programming problems that can be solved with acceptable efficiency by primal-dual interior-point methods. The experimental results on artificially generated data sets and benchmark data sets demonstrate the advantages of TOCC.     

Covariance-guided One-Class Support Vector Machine

In one-class classification, the low variance directions in the training data carry crucial information to build a good model of the target class. Boundary-based methods like One-Class Support Vector Machine (OSVM) preferentially separates the data from outliers along the large variance directions. On the other hand, retaining only the low variance directions can result in sacrificing some initial properties of the original data and is not desirable, specially in case of limited training samples. This paper introduces a Covariance-guided One-Class Support Vector Machine (COSVM) classification method which emphasizes the low variance projectional directions of the training data without compromising any important characteristics. COSVM improves upon the OSVM method by controlling the direction of the separating hyperplane through incorporation of the estimated covariance matrix from the training data. Our proposed method is a convex optimization problem resulting in one global optimum solution which can be solved efficiently with the help of existing numerical methods. The method also keeps the principal structure of the OSVM method intact, and can be implemented easily with the existing OSVM libraries. Comparative experimental results with contemporary one-class classifiers on numerous artificial and benchmark datasets demonstrate that our method results in significantly better classification performance.     

One-class remote sensing classification: one-class vs. binary classifiers

Many applications of remote sensing only require the classification of a single land type. This is known as the one-class classification problem and it can be performed using either binary classifiers, by treating all other classes as the negative class, or one-class classifiers which only consider the class of interest. The key difference between these two approaches is in their training data and the amount of effort needed to produce it. Binary classifiers require an exhaustively labelled training data set while one-class classifiers are trained using samples of just the class of interest. Given ample and complete training data, binary classifiers generally outperform one-class classifiers. However, what is not clear is which approach is more accurate when given the same amount of labelled training data. That is, for a fixed labelling effort, is it better to use a binary or one-class classifier. This is the question we consider in this article. We compare several binary classifiers, including backpropagation neural networks, support vector machines, and maximum likelihood classifiers, with two one-class classifiers, one-class SVM, and presence and background learning (PBL), on the problem of one-class classification in high-resolution remote sensing imagery. We show that, given a fixed labelling budget, PBL consistently outperforms the other methods. This advantage stems from the fact that PBL is a positive-unlabelled method in which large amounts of readily available unlabelled data is incorporated into the training phase, allowing the classifier to model the negative class more effectively.     

基于单簇聚类的数据描述

文中提出了一种基于单簇可能性C-均值聚类(Possibilistjc C-Means,PCM)的数据描述方法并用于单分类.训练时,其首先进行P1M(PCM,C值取1)聚类,得到所有训练样本对目标类的隶属度;然后设置隶属度阈值,形成相应的数据描述进行单分类.分类时,计算新样本对目标类的隶属度,若其隶属度小于该阈值则判为异常,否则为正常.该方法和当前流行的支持向量域数据描述方法以及Parzen方法窗具有类似的参数配置和相当的分类性能,由此提供了另一种单分类学习算法.值得指出的是,尽管是PCM的一个特例,但P1M拥有PCM一般不具备的全局最优特性,而该特性对解决实际问题十分重要.     

Rule-based OneClass-DS learning algorithm

One-class learning algorithms are used in situations when training data are available only for one class, called target class. Data for other class(es), called outliers, are not available. One-class learning algorithms are used for detecting outliers, or novelty, in the data. The common approach in one-class learning is to use density estimation techniques or adapt standard classification algorithms to define a decision boundary that encompasses only the target data. In this paper, we introduce OneClass-DS learning algorithm that combines rule-based classification with greedy search algorithm based on density of features. Its performance is tested on 25 data sets and compared with eight other one-class algorithms; the results show that it performs on par with those algorithms.     

一种新的多类支持向量机算法

提出了一种新的多类支持向量机算法OC-K-SVM.对k类分类问题,该方法构造了k个分类器,每一个分类器只对一类样本进行训练.使用Benchmark的数据集进行了初步的实验,实验结果验证了算法的有效性.     

A new method for anomaly detection based on non-convex boundaries with random two-dimensional projections

The implementation of anomaly detection systems represents a key problem that has been focusing the efforts of scientific community. In this context, the use one-class techniques to model a training set of non-anomalous objects can play a significant role. One common approach to face the one-class problem is based on determining the geometric boundaries of the target set. More specifically, the use of convex hull combined with random projections offers good results but presents low performance when it is applied to non-convex sets. Then, this work proposes a new method that face this issue by implementing non-convex boundaries over each projection. The proposal was assessed and compared with the most common one-class techniques, over different sets, obtaining successful results.     

Non-linear metric learning using pairwise similarity and dissimilarity constraints and the geometrical structure of data

The problem of clustering with side information has received much recent attention and metric learning has been considered as a powerful approach to this problem. Until now, various metric learning methods have been proposed for semi-supervised clustering. Although some of the existing methods can use both positive (must-link) and negative (cannot-link) constraints, they are usually limited to learning a linear transformation (i.e., finding a global Mahalanobis metric). In this paper, we propose a framework for learning linear and non-linear transformations efficiently. We use both positive and negative constraints and also the intrinsic topological structure of data. We formulate our metric learning method as an appropriate optimization problem and find the global optimum of this problem. The proposed non-linear method can be considered as an efficient kernel learning method that yields an explicit non-linear transformation and thus shows out-of-sample generalization ability. Experimental results on synthetic and real-world data sets show the effectiveness of our metric learning method for semi-supervised clustering tasks.     

人脸确认的动态支持向量数据描述方法

人脸的确认实质上是一个一类分类问题或野点检测问题,即只需要精确地描述某一类样本的分布,而将该类样本之外大范围内的样本点视为野点.为了能精确地描述某一类样本的分布,在对国内外现有统计学习理论和核方法进行研究的基础上,针对＂人脸确认＂这一特定的应用对象,分析了已有的一类分类算法,即支持向量数据描述方法在处理动态样本中存在的不足,进而指出,随着训练样本数目的增加,该算法会因为过大的优化规模而无法实际操作,为此提出了用于人脸确认的动态支持向量数据描述算法.由于新算法在优化过程中,仅需要考虑待检测样本和原有支持向量集,从而可以大大降低优化过程中涉及的运算规模和内存需求,进而可保证人脸确认过程中的实时性与动态性要求.     

基于填充函数法训练BP神经网络的车牌字符识别算法

字符识别是车牌识别系统的一个关键问题。常用方法收敛速度慢,易陷入局部最优,用全局优化填充函数法训练BP神经网络的车牌字符识别算法可以跳出当前局部极小点,得到一个更低的极小点,重复此过程得到全局极小点,从而提高算法全局寻优能力。实验表明,该算法具有识别率高、识别速度快、车牌定位准确的特点,取得良好的运行效果。     

基于最近邻的遥感影像单类信息提取

遥感影像单类信息提取是一种特殊的分类,旨在训练和提取单一兴趣类别。研究了基于最近邻分类器的单类信息提取方法,包括类别划分和样本选择问题。首先分析论证了最近邻方法提取单类信息只与所选择的样本相关,而与类别划分无关,因此可以将单类信息提 取作为二类分类问题进行处理。然后在二类分类问题中,根据空间和特征邻近性选择非兴趣类别的部分训练样本,简化了分类过程。实验结果表明,所提出的方法可以有效实现遥感影像单类信息的提取。     

Imbalanced classification using support vector machine ensemble

Imbalanced data sets often have detrimental effects on the performance of a conventional support vector machine (SVM). To solve this problem, we adopt both strategies of modifying the data distribution and adjusting the classifier. Both minority and majority classes are resampled to increase the generalization ability. For minority class, an one-class support vector machine model combined with synthetic minority oversampling technique is used to oversample the support vector instances. For majority class, we propose a new method to decompose the majority class into clusters and remove two clusters using a distance measure to lessen the effect of outliers. The remaining clusters are used to build an SVM ensemble with the oversampled minority patterns, the SVM ensemble can achieve better performance by considering potentially suboptimal solutions. Experimental results on benchmark data sets are provided to illustrate the effectiveness of the proposed method.     

Approximate polytope ensemble for one-class classification

In this work, a new one-class classification ensemble strategy called approximate polytope ensemble is presented. The main contribution of the paper is threefold. First, the geometrical concept of convex hull is used to define the boundary of the target class defining the problem. Expansions and contractions of this geometrical structure are introduced in order to avoid over-fitting. Second, the decision whether a point belongs to the convex hull model in high dimensional spaces is approximated by means of random projections and an ensemble decision process. Finally, a tiling strategy is proposed in order to model non-convex structures. Experimental results show that the proposed strategy is significantly better than state of the art one-class classification methods on over 200 datasets.     

General support vector representation machine for one-class classification of non-stationary classes

Novelty detection, also referred to as one-class classification, is the process of detecting ‘abnormal’ behavior in a system by learning the ‘normal’ behavior. Novelty detection has been of particular interest to researchers in domains where it is difficult or expensive to find examples of abnormal behavior (such as in medical/equipment diagnosis and IT network surveillance). Effective representation of normal data is of primary interest in pursuing one-class classification. While the literature offers several methods for one-class classification, very few methods can support representation of non-stationary classes without making stringent assumptions about the class distribution. This paper proposes a one-class classification method for non-stationary classes using a modified support vector machine and an efficient online version for reducing computational time. The presented method is applied to several simulated datasets and actual data from a drilling machine. In addition, we present comparison results with other methods that demonstrate its superior performance.     

Estimation of high-density regions using One-Class Neighbor Machines

In this paper, we investigate the problem of estimating high-density regions from univariate or multivariate data samples. We estimate minimum volume sets, whose probability is specified in advance, known in the literature as density contour clusters. This problem is strongly related to one-class support vector machines (OCSVM). We propose a new method to solve this problem, the one-class neighbor machine (OCNM) and we show its properties. In particular, the OCNM solution asymptotically converges to the exact minimum volume set prespecified. Finally, numerical results illustrating the advantage of the new method are shown.     

一种基于L2-SVM 的多视角核心向量机

核化一类硬划分SVDD、一/二类L2-SVM、L2 支持向量回归和Ranking SVM均已被证明是中心约束最小包含球. 这里将多视角学习引入核化L2-SVM, 提出核化两类多视角L2-SVM (Multi-view L2-SVM), 并证明该核化两类Multi-view L2-SVM 亦为中心约束最小包含球, 进而提出一种多视角核心向量机MvCVM. 所提出的Multi-view L2-SVM 和MvCVM既考虑了视角之间的差异性, 又考虑了视角之间的关联性, 使得分类器在各个视角上的学习结果趋于一致. 人造多视角数据集和真实多视角数据集的实验均表明了Multi-view L2-SVM 和MvCVM方法的有效性.

     

Deep Domain-Adversarial Anomaly Detection With One-Class Transfer Learning

Despite the big success of transfer learning techniques in anomaly detection, it is still challenging to achieve good transition of detection rules merely based on the preferred data in the anomaly detection with one-class classification, especially for the data with a large distribution difference. To address this challenge,a novel deep one-class transfer learning algorithm with domain-adversarial training is proposed in this paper. First, by integrating a hypersphere adaptation constraint into...