The Experts below are selected from a list of 108690 Experts worldwide ranked by ideXlab platform
Fabrizio Angiulli - One of the best experts on this subject based on the ideXlab platform.
-
Prototype-Based Domain Description for One-Class Classification
IEEE transactions on pattern analysis and machine intelligence, 2012Co-Authors: Fabrizio AngiulliAbstract:This work introduces the Prototype-based Domain Description rule (PDD) one-class classifier. PDD is a nearest neighbor-based classifier since it accepts objects on the basis of their nearest neighbor distances in a reference set of objects, also called prototypes. For a suitable choice of the prototype set, the PDD classifier is equivalent to another nearest neighbor-based one-class classifier, namely, the NNDD classifier. Moreover, it generalizes statistical tests for outlier detection. The concept of a PDD consistent subset is introduced, which exploits only a selected subset of the training set. It is shown that computing a minimum size PDD consistent subset is, in general, not approximable within any constant factor. A logarithmic approximation factor algorithm, called the CPDD algorithm, for computing a minimum size PDD consistent subset is then introduced. In order to efficiently manage very large data sets, a variant of the basic rule, called Fast CPDD, is also presented. Experimental results show that the CPDD rule sensibly improves over the CNNDD classifier, namely the condensed variant of NNDD, in terms of size of the subset while guaranteeing a comparable classification quality, that it is competitive over other one-class classification methods and is suitable to classify large data sets.
-
ECAI - Prototype-based Domain Description
2008Co-Authors: Fabrizio AngiulliAbstract:In this work a novel one-class classifier, namely the Prototype-based Domain Description rule (PDD), is presented. The PDD classifier is equivalent to the NNDD rule under the infinity Minkowski metric for a suitable choice of the prototype set. The concept of PDD consistent subset is introduced and it is shown that computing a minimum size PDD consistent subset is in general not approximable within any constant factor. A logarithmic approximation factor algorithm, called the CPDD algorithm, for computing a minimum size PDD consistent subset is then introduced. The CPDD algorithm has some parameters which allow to tune the trade off between accuracy and size of the model. Experimental results show that the CPDD rule sensibly improves over the CNNDD classifier in terms of size of the subset, while guaranteeing a comparable classification quality.
-
Condensed Nearest Neighbor Data Domain Description
IEEE transactions on pattern analysis and machine intelligence, 2007Co-Authors: Fabrizio AngiulliAbstract:A simple yet effective unsupervised classification rule to discriminate between normal and abnormal data is based on accepting test objects whose nearest neighbors' distances in a reference data set, assumed to model normal behavior, lie within a certain threshold. This work investigates the effect of using a subset of the original data set as the reference set of the classifier. With this aim, the concept of a reference-consistent subset is introduced and it is shown that finding the minimum-cardinality reference-consistent subset is intractable. Then, the condensed nearest neighbor Domain Description (CNNDD) algorithm is described, which computes a reference-consistent subset with only two reference set passes. Experimental results revealed the advantages of condensing the data set and confirmed the effectiveness of the proposed approach. A thorough comparison with related methods was accomplished, pointing out the strengths and weaknesses of one-class nearest-neighbor-based training-set-consistent condensation.
-
condensed nearest neighbor data Domain Description
Intelligent Data Analysis, 2005Co-Authors: Fabrizio AngiulliAbstract:A popular method to discriminate between normal and abnormal data is based on accepting test objects whose nearest neighbors distances in a reference data set lie within a certain threshold. In this work we investigate the possibility of using as reference set a subset of the original data set. We discuss relationship between reference set size and generalization, and show that finding the minimum cardinality reference consistent subset is intractable. Then, we describe an algorithm that computes a reference consistent subset with only two reference set passes. Experimental results confirm the effectiveness of the approach.
-
IDA - Condensed nearest neighbor data Domain Description
Lecture Notes in Computer Science, 2005Co-Authors: Fabrizio AngiulliAbstract:A popular method to discriminate between normal and abnormal data is based on accepting test objects whose nearest neighbors distances in a reference data set lie within a certain threshold. In this work we investigate the possibility of using as reference set a subset of the original data set. We discuss relationship between reference set size and generalization, and show that finding the minimum cardinality reference consistent subset is intractable. Then, we describe an algorithm that computes a reference consistent subset with only two reference set passes. Experimental results confirm the effectiveness of the approach.
Rasmus Larsen - One of the best experts on this subject based on the ideXlab platform.
-
On the regularization path of the support vector Domain Description
Pattern Recognition Letters, 2010Co-Authors: Michael Sass Hansen, Karl Sjostrand, Rasmus LarsenAbstract:The internet and a growing number of increasingly sophisticated measuring devices make vast amounts of data available in many applications. However, the dimensionality is often high, and the time available for manual labelling scarce. Methods for unsupervised novelty detection are a great step towards meeting these challenges, and the support vector Domain Description has already shown its worth in this field. The method has recently received more attention, since it has been shown that the regularization path is piece-wise linear, and can be calculated efficiently. The presented work restates the new findings in a manner which permits the calculation with O(n.n"B) complexity in each iteration step instead of On^2+n"B^3, where n is the number of data points and n"B is the number of boundary points. This is achieved by updating and downdating the system matrix to avoid redundant calculations. We believe this will further promote the use of this method.
-
a path algorithm for the support vector Domain Description and its application to medical imaging
Medical Image Analysis, 2007Co-Authors: Karl Sjostrand, Michael Sass Hansen, Henrik B W Larsson, Rasmus LarsenAbstract:Abstract The support vector Domain Description is a one-class classification method that estimates the distributional support of a data set. A flexible closed boundary function is used to separate trustworthy data on the inside from outliers on the outside. A single regularization parameter determines the shape of the boundary and the proportion of observations that are regarded as outliers. Picking an appropriate amount of regularization is crucial in most applications but is, for computational reasons, commonly limited to a small collection of parameter values. This paper presents an algorithm where the solutions for all possible values of the regularization parameter are computed at roughly the same computational complexity previously required to obtain a single solution. Such a collection of solutions is known as a regularization path. Knowledge of the entire regularization path not only aids model selection, but may also provide new information about a data set. We illustrate this potential of the method in two applications; one where we establish a sensible ordering among a set of corpora callosa outlines, and one where ischemic segments of the myocardium are detected in patients with acute myocardial infarction.
-
Medical Imaging: Image Processing - Ischemic Segment Detection using the Support Vector Domain Description
Medical Imaging 2007: Image Processing, 2007Co-Authors: Michael Sass Hansen, Karl Sjostrand, Hildur Ólafsdóttir, Søren Gylling Hemmingsen Erbou, Mikkel Bille Stegmann, H. B. W. Larsson, Rasmus LarsenAbstract:Myocardial perfusion Magnetic Resonance (MR) imaging has proven to be a powerful method to assess coronary artery diseases. The current work presents a novel approach to the analysis of registered sequences of myocardial perfusion MR images. A previously reported active appearance model (AAM) based segmentation and registration of the myocardium provided pixel-wise signal intensity curves that were analyzed using the Support Vector Domain Description (SVDD). In contrast to normal SVDD, the entire regularization path was calculated and used to calculate a generalized distance, which is used to discriminate between ischemic and healthy tissue. The results corresponded well to the ischemic segments found by assessment of the three common perfusion parameters; maximum upslope, peak and time-to-peak obtained pixel-wise.
-
the entire regularization path for the support vector Domain Description
Medical Image Computing and Computer-Assisted Intervention, 2006Co-Authors: Karl Sjostrand, Rasmus LarsenAbstract:The support vector Domain Description is a one-class classification method that estimates the shape and extent of the distribution of a data set. This separates the data into outliers, outside the decision boundary, and inliers on the inside. The method bears close resemblance to the two-class support vector machine classifier. Recently, it was shown that the regularization path of the support vector machine is piecewise linear, and that the entire path can be computed efficiently. This paper shows that this property carries over to the support vector Domain Description. Using our results the solution to the one-class classification can be obtained for any amount of regularization with roughly the same computational complexity required to solve for a particularly value of the regularization parameter. The possibility of evaluating the results for any amount of regularization not only offers more accurate and reliable models, but also makes way for new applications. We illustrate the potential of the method by determining the order of inclusion in the model for a set of corpora callosa outlines.
-
MICCAI (1) - The entire regularization path for the support vector Domain Description
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Inte, 2006Co-Authors: Karl Sjostrand, Rasmus LarsenAbstract:The support vector Domain Description is a one-class classification method that estimates the shape and extent of the distribution of a data set. This separates the data into outliers, outside the decision boundary, and inliers on the inside. The method bears close resemblance to the two-class support vector machine classifier. Recently, it was shown that the regularization path of the support vector machine is piecewise linear, and that the entire path can be computed efficiently. This paper shows that this property carries over to the support vector Domain Description. Using our results the solution to the one-class classification can be obtained for any amount of regularization with roughly the same computational complexity required to solve for a particularly value of the regularization parameter. The possibility of evaluating the results for any amount of regularization not only offers more accurate and reliable models, but also makes way for new applications. We illustrate the potential of the method by determining the order of inclusion in the model for a set of corpora callosa outlines.
Ulf Brefeld - One of the best experts on this subject based on the ideXlab platform.
-
active and semi supervised data Domain Description
European conference on Machine Learning, 2009Co-Authors: Nico Gornitz, Marius Kloft, Ulf BrefeldAbstract:Data Domain Description techniques aim at deriving concise Descriptions of objects belonging to a category of interest. For instance, the support vector Domain Description (SVDD) learns a hypersphere enclosing the bulk of provided unlabeled data such that points lying outside of the ball are considered anomalous. However, relevant information such as expert and background knowledge remain unused in the unsupervised setting. In this paper, we rephrase data Domain Description as a semi-supervised learning task, that is, we propose a semi-supervised generalization of data Domain Description (SSSVDD) to process unlabeled and labeled examples. The corresponding optimization problem is non-convex. We translate it into an unconstraint, continuous problem that can be optimized accurately by gradient-based techniques. Furthermore, we devise an effective active learning strategy to query low-confidence observations. Our empirical evaluation on network intrusion detection and object recognition tasks shows that our SSSVDDs consistently outperform baseline methods in relevant learning settings.
-
ECML/PKDD (1) - Active and Semi-supervised Data Domain Description
Machine Learning and Knowledge Discovery in Databases, 2009Co-Authors: Nico Gornitz, Marius Kloft, Ulf BrefeldAbstract:Data Domain Description techniques aim at deriving concise Descriptions of objects belonging to a category of interest. For instance, the support vector Domain Description (SVDD) learns a hypersphere enclosing the bulk of provided unlabeled data such that points lying outside of the ball are considered anomalous. However, relevant information such as expert and background knowledge remain unused in the unsupervised setting. In this paper, we rephrase data Domain Description as a semi-supervised learning task, that is, we propose a semi-supervised generalization of data Domain Description (SSSVDD) to process unlabeled and labeled examples. The corresponding optimization problem is non-convex. We translate it into an unconstraint, continuous problem that can be optimized accurately by gradient-based techniques. Furthermore, we devise an effective active learning strategy to query low-confidence observations. Our empirical evaluation on network intrusion detection and object recognition tasks shows that our SSSVDDs consistently outperform baseline methods in relevant learning settings.
Karl Sjostrand - One of the best experts on this subject based on the ideXlab platform.
-
On the regularization path of the support vector Domain Description
Pattern Recognition Letters, 2010Co-Authors: Michael Sass Hansen, Karl Sjostrand, Rasmus LarsenAbstract:The internet and a growing number of increasingly sophisticated measuring devices make vast amounts of data available in many applications. However, the dimensionality is often high, and the time available for manual labelling scarce. Methods for unsupervised novelty detection are a great step towards meeting these challenges, and the support vector Domain Description has already shown its worth in this field. The method has recently received more attention, since it has been shown that the regularization path is piece-wise linear, and can be calculated efficiently. The presented work restates the new findings in a manner which permits the calculation with O(n.n"B) complexity in each iteration step instead of On^2+n"B^3, where n is the number of data points and n"B is the number of boundary points. This is achieved by updating and downdating the system matrix to avoid redundant calculations. We believe this will further promote the use of this method.
-
a path algorithm for the support vector Domain Description and its application to medical imaging
Medical Image Analysis, 2007Co-Authors: Karl Sjostrand, Michael Sass Hansen, Henrik B W Larsson, Rasmus LarsenAbstract:Abstract The support vector Domain Description is a one-class classification method that estimates the distributional support of a data set. A flexible closed boundary function is used to separate trustworthy data on the inside from outliers on the outside. A single regularization parameter determines the shape of the boundary and the proportion of observations that are regarded as outliers. Picking an appropriate amount of regularization is crucial in most applications but is, for computational reasons, commonly limited to a small collection of parameter values. This paper presents an algorithm where the solutions for all possible values of the regularization parameter are computed at roughly the same computational complexity previously required to obtain a single solution. Such a collection of solutions is known as a regularization path. Knowledge of the entire regularization path not only aids model selection, but may also provide new information about a data set. We illustrate this potential of the method in two applications; one where we establish a sensible ordering among a set of corpora callosa outlines, and one where ischemic segments of the myocardium are detected in patients with acute myocardial infarction.
-
Medical Imaging: Image Processing - Ischemic Segment Detection using the Support Vector Domain Description
Medical Imaging 2007: Image Processing, 2007Co-Authors: Michael Sass Hansen, Karl Sjostrand, Hildur Ólafsdóttir, Søren Gylling Hemmingsen Erbou, Mikkel Bille Stegmann, H. B. W. Larsson, Rasmus LarsenAbstract:Myocardial perfusion Magnetic Resonance (MR) imaging has proven to be a powerful method to assess coronary artery diseases. The current work presents a novel approach to the analysis of registered sequences of myocardial perfusion MR images. A previously reported active appearance model (AAM) based segmentation and registration of the myocardium provided pixel-wise signal intensity curves that were analyzed using the Support Vector Domain Description (SVDD). In contrast to normal SVDD, the entire regularization path was calculated and used to calculate a generalized distance, which is used to discriminate between ischemic and healthy tissue. The results corresponded well to the ischemic segments found by assessment of the three common perfusion parameters; maximum upslope, peak and time-to-peak obtained pixel-wise.
-
the entire regularization path for the support vector Domain Description
Medical Image Computing and Computer-Assisted Intervention, 2006Co-Authors: Karl Sjostrand, Rasmus LarsenAbstract:The support vector Domain Description is a one-class classification method that estimates the shape and extent of the distribution of a data set. This separates the data into outliers, outside the decision boundary, and inliers on the inside. The method bears close resemblance to the two-class support vector machine classifier. Recently, it was shown that the regularization path of the support vector machine is piecewise linear, and that the entire path can be computed efficiently. This paper shows that this property carries over to the support vector Domain Description. Using our results the solution to the one-class classification can be obtained for any amount of regularization with roughly the same computational complexity required to solve for a particularly value of the regularization parameter. The possibility of evaluating the results for any amount of regularization not only offers more accurate and reliable models, but also makes way for new applications. We illustrate the potential of the method by determining the order of inclusion in the model for a set of corpora callosa outlines.
-
MICCAI (1) - The entire regularization path for the support vector Domain Description
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Inte, 2006Co-Authors: Karl Sjostrand, Rasmus LarsenAbstract:The support vector Domain Description is a one-class classification method that estimates the shape and extent of the distribution of a data set. This separates the data into outliers, outside the decision boundary, and inliers on the inside. The method bears close resemblance to the two-class support vector machine classifier. Recently, it was shown that the regularization path of the support vector machine is piecewise linear, and that the entire path can be computed efficiently. This paper shows that this property carries over to the support vector Domain Description. Using our results the solution to the one-class classification can be obtained for any amount of regularization with roughly the same computational complexity required to solve for a particularly value of the regularization parameter. The possibility of evaluating the results for any amount of regularization not only offers more accurate and reliable models, but also makes way for new applications. We illustrate the potential of the method by determining the order of inclusion in the model for a set of corpora callosa outlines.
Chang-dong Wang - One of the best experts on this subject based on the ideXlab platform.
-
multi local model image set matching based on Domain Description
Pattern Recognition, 2014Co-Authors: Qingsong Zeng, Jianhuang Lai, Chang-dong WangAbstract:Image set matching attracted increasing attention in the field of pattern recognition. Recently, there are a number of effective image set-based matching methods under controlled environment. However in the more complex environment, like multi-view and illumination changed, it is still a challenging problem to develop unsupervised image set matching method to handle multi-local model data. To solve this problem, in this paper, we present a novel multi-local model image set matching method based on data Description techniques. First, every image set is divided into multi-local models, and each local model corresponds to a data Domain, that is, we innovatively train a support vector data Domain to describe each local model by means of the excellent data Description ability of support vector data Domain, hence each image set can be expressed by a plurality of support vector data Domain. Second, a new similarity measure based on Domain-Domain distance is proposed, and then the distance between two image sets is converted to integrate the distance between pair-wise Domains. Finally, the proposed method is evaluated on both set-based face recognition and object classification tasks. Extensive experimental results show that the proposed method outperforms other state of the art set-based matching methods in three public video databases. HighlightsWe propose a novel multi-local mode image set matching algorithm.It is the first attempt to introduce the SVDD to image set matching.We solve the set matching as an optimization problem via Domain Description.We formulate the image set matching as the distances between pair-wise Domains.Experimental results demonstrate the superior performance of our approach.
-
Position regularized Support Vector Domain Description
Pattern Recognition, 2013Co-Authors: Chang-dong Wang, Jianhuang LaiAbstract:Support Vector Domain Description (SVDD) is an effective method for describing a set of objects. As a basic tool, several application-oriented extensions have been developed, such as support vector clustering (SVC), SVDD-based k-Means (SVDDk-Means) and support vector based algorithm for clustering data streams (SVStream). Despite its significant success, one inherent drawback is that the Description is very sensitive to the selection of the trade-off parameter, which is hard to estimate in practice and affects the extensive approaches significantly. To tackle this problem, we propose a novel Position regularized Support Vector Domain Description (PSVDD). In the proposed PSVDD, the complexity of the sphere surface is adaptively regularized by assigning a position-based weighting to each data point, which is computed according to the distance between the corresponding feature space image and the mean of feature space images. To demonstrate the effectiveness of the proposed PSVDD, we apply the position-based weighting to improve two important clustering extensions, i.e., SVC and SVDDk-Means, which respectively result in two new clustering approaches termed PSVC and PSVDDk-Means. Experimental results on several real-world data sets validate the significant improvement achieved by PSVC and PSVDDk-Means.