Results 1  10
of
85
Estimating the Support of a HighDimensional Distribution
, 1999
"... Suppose you are given some dataset drawn from an underlying probability distribution P and you want to estimate a "simple" subset S of input space such that the probability that a test point drawn from P lies outside of S is bounded by some a priori specified between 0 and 1. We propo ..."
Abstract

Cited by 766 (29 self)
 Add to MetaCart
Suppose you are given some dataset drawn from an underlying probability distribution P and you want to estimate a "simple" subset S of input space such that the probability that a test point drawn from P lies outside of S is bounded by some a priori specified between 0 and 1. We propose a method to approach this problem by trying to estimate a function f which is positive on S and negative on the complement. The functional form of f is given by a kernel expansion in terms of a potentially small subset of the training data; it is regularized by controlling the length of the weight vector in an associated feature space. The expansion coefficients are found by solving a quadratic programming problem, which we do by carrying out sequential optimization over pairs of input patterns. We also provide a preliminary theoretical analysis of the statistical performance of our algorithm. The algorithm is a natural extension of the support vector algorithm to the case of unlabelled d...
Anomaly Detection: A Survey
, 2007
"... Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and c ..."
Abstract

Cited by 511 (5 self)
 Add to MetaCart
(Show Context)
Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. We have grouped existing techniques into different categories based on the underlying approach adopted by each technique. For each category we have identified key assumptions, which are used by the techniques to differentiate between normal and anomalous behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain. For each category, we provide a basic anomaly detection technique, and then show how the different existing techniques in that category are variants of the basic technique. This template provides an easier and succinct understanding of the techniques belonging to each category. Further, for each category, we identify the advantages and disadvantages of the techniques in that category. We also provide a discussion on the computational complexity of the techniques since it is an important issue in real application domains. We hope that this survey will provide a better understanding of the di®erent directions in which research has been done on this topic, and how techniques developed in one area can be applied in domains for which they were not intended to begin with.
Novelty Detection: A Review  Part 1: Statistical Approaches
 Signal Processing
, 2003
"... Novelty detection is the identification of new or unknown data or signal that a machine learning system is not aware of during training. Novelty detection is one of the fundamental requirements of a good classification or identification system since sometimes the test data contains information abou ..."
Abstract

Cited by 198 (0 self)
 Add to MetaCart
Novelty detection is the identification of new or unknown data or signal that a machine learning system is not aware of during training. Novelty detection is one of the fundamental requirements of a good classification or identification system since sometimes the test data contains information about objects that were not known at the time of training the model. In this paper we provide stateof theart review in the area of novelty detection based on statistical approaches. The second part paper details novelty detection using neural networks. As discussed, there are a multitude of applications where novelty detection is extremely important including signal processing, computer vision, pattern recognition, data mining, and robotics.
A principled approach to detecting surprising events in video
 in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR
, 2005
"... Primates demonstrate unparalleled ability at rapidly orienting towards important events in complex dynamic environments. During rapid guidance of attention and gaze towards potential objects of interest or threats, often there is no time for detailed visual analysis. Thus, heuristic computations are ..."
Abstract

Cited by 117 (6 self)
 Add to MetaCart
(Show Context)
Primates demonstrate unparalleled ability at rapidly orienting towards important events in complex dynamic environments. During rapid guidance of attention and gaze towards potential objects of interest or threats, often there is no time for detailed visual analysis. Thus, heuristic computations are necessary to locate the most interesting events in quasi realtime. We present a new theory of sensory surprise, which provides a principled and computable shortcut to important information. We develop a model that computes instantaneous lowlevel surprise at every location in video streams. The algorithm significantly correlates with eye movements of two humans watching complex video clips, including television programs (17,936 frames, 2,152 saccadic gaze shifts). The system allows more sophisticated and timeconsuming image analysis to be efficiently focused onto the most surprising subsets of the incoming data. 1.
Novelty Detection: A Review  Part 2: Neural network based approaches
 Signal Processing
, 2003
"... Novelty detection is the ident ification of new or unknown data or signal that a machine learning system is not aware of during training. In this paper we focus on neural network based approaches for novelty detection. Statistical approaches are covered in partI paper. ..."
Abstract

Cited by 103 (0 self)
 Add to MetaCart
Novelty detection is the ident ification of new or unknown data or signal that a machine learning system is not aware of during training. In this paper we focus on neural network based approaches for novelty detection. Statistical approaches are covered in partI paper.
A classification framework for anomaly detection
 J. Machine Learning Research
, 2005
"... One way to describe anomalies is by saying that anomalies are not concentrated. This leads to the problem of finding level sets for the data generating density. We interpret this learning problem as a binary classification problem and compare the corresponding classification risk with the standard p ..."
Abstract

Cited by 71 (6 self)
 Add to MetaCart
(Show Context)
One way to describe anomalies is by saying that anomalies are not concentrated. This leads to the problem of finding level sets for the data generating density. We interpret this learning problem as a binary classification problem and compare the corresponding classification risk with the standard performance measure for the density level problem. In particular it turns out that the empirical classification risk can serve as an empirical performance measure for the anomaly detection problem. This allows us to compare different anomaly detection algorithms empirically, i.e. with the help of a test set. Based on the above interpretation we then propose a support vector machine (SVM) for anomaly detection. Finally, we establish universal consistency for this SVM and report some experiments which compare our SVM to other commonly used methods including the standard oneclass SVM. 1
Structural Health Monitoring Using Statistical Pattern Recognition Techniques
, 2001
"... This paper casts structural health monitoring in the context of a statistical pattern recognition paradigm. Two pattern recognition techniques based on time series analysis are applied to fiber optic strain gauge data obtained from two different structural conditions of a surfaceeffect fast patrol ..."
Abstract

Cited by 63 (8 self)
 Add to MetaCart
This paper casts structural health monitoring in the context of a statistical pattern recognition paradigm. Two pattern recognition techniques based on time series analysis are applied to fiber optic strain gauge data obtained from two different structural conditions of a surfaceeffect fast patrol boat. The first technique is based on a twostage time series analysis combining AutoRegressive (AR) and AutoRegressive with eXogenous inputs (ARX) prediction models. The second technique employs an outlier analysis with the Mahalanobis distance measure. The main objective is to extract features and construct a statistical model that distinguishes the signals recorded under the different structural conditions of the boat. These two techniques were successfully applied to the patrol boat data clearly distinguishing data sets obtained from different structural conditions
Kernel PCA for novelty detection
 Pattern Recognition
"... Kernel principal component analysis (kernel PCA) is a nonlinear extension of PCA. This study introduces and investigates the use of kernel PCA for novelty detection. Training data are mapped into an infinitedimensional feature space. In this space, kernel PCA extracts the principal components of t ..."
Abstract

Cited by 51 (1 self)
 Add to MetaCart
(Show Context)
Kernel principal component analysis (kernel PCA) is a nonlinear extension of PCA. This study introduces and investigates the use of kernel PCA for novelty detection. Training data are mapped into an infinitedimensional feature space. In this space, kernel PCA extracts the principal components of the data distribution. The squared distance to the corresponding principal subspace is the measure for novelty. This new method demonstrated a competitive performance on twodimensional synthetic distributions and on two realworld data sets: handwritten digits and breastcancer cytology.
Combining Oneclass Classifiers
 in Proc. Multiple Classifier Systems, 2001
, 2001
"... . In the problem of oneclass classification target objects should ..."
Abstract

Cited by 30 (2 self)
 Add to MetaCart
(Show Context)
. In the problem of oneclass classification target objects should