A review of novelty detection

Kernel Fisher Discriminants for Outlier Detection

Röth, V. (2006) · Neural Computation · doi:10.1162/089976606775774679 · Cited by 64

Taming the Curse of Dimensionality in Kernels and Novelty Detection

Evangelista, P. F., Embrechts, M. J., & Szymanski, B. K. (2006) · Springer eBooks · doi:10.1007/3-540-31662-0_33 · Cited by 63

Real-time object classification and novelty detection for collaborative video surveillance

Diehl, C., & Hampshire, J. B. (2003) · doi:10.1109/ijcnn.2002.1007557 · Cited by 62

Novelty detection in a changing environment: A negative selection approach

Surace, C., & Worden, K. (2009) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2009.09.009 · Cited by 61

Hierarchical Anomaly Detection in Distributed Large-Scale Sensor Networks

Chatzigiannakis, V., Papavassiliou, S., Grammatikou, M., & Maglaris, V. (2006) · IEEE Symposium on Computers and Communications (ISCC) · doi:10.1109/iscc.2006.1691116 · Cited by 60

Anomaly Detection in Large Sets of High-Dimensional Symbol Sequences

Budalakoti, S., Srivastava, A. N., Akella, R., & Turkov, E. (2006) · NASA Technical Reports Server (NASA) · Cited by 59

Outlier identification and market segmentation using kernel-based clustering techniques

Wang, C.-H. (2008) · Expert Systems with Applications · doi:10.1016/j.eswa.2008.02.037 · Cited by 59

Finding centric local outliers in categorical/numerical spaces

Yu, J. X., Qian, W., Lü, H., & Zhou, A. (2005) · Knowledge and Information Systems · doi:10.1007/s10115-005-0197-6 · Cited by 59

An Investigation of the Negative Selection Algorithm for Fault Detection in Refrigeration Systems

Taylor, D. W., & Corne, D. (2003) · Lecture notes in computer science · doi:10.1007/978-3-540-45192-1_4 · Cited by 58

Implicit learning in autoencoder novelty assessment

Thompson, B. B., Marks, R. J., Choi, J. J., El-Sharkawi, M. A., Huang, M.-Y., & Bunje, C. (2003) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2002.1007605 · Cited by 58

An Optimization Model for Outlier Detection in Categorical DataPreprint

He, Z., Xu, X., & Deng, S. (2005) · arXiv (Cornell University) · doi:10.48550/arxiv.cs/0503081 · Cited by 58

State-of-the-Art in Bayesian Changepoint Detection

Tartakovsky, A. G., & Moustakides, G. V. (2010) · Sequential Analysis · doi:10.1080/07474941003740997 · Cited by 57

Graph-based anomaly detection

Noble, C. C., & Cook, D. J. (2003) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/956804.956831 · Cited by 56

Markov models for identification of significant episodes

Gwadera, R., Atallah, M. J., & Szpankowski, W. (2005) · SIAM International Conference on Data Mining · doi:10.1137/1.9781611972757.36 · Cited by 55

An Empirical Bayes Approach to Detect Anomalies in Dynamic Multidimensional Arrays

Agarwal, D. (2006) · Proceedings · doi:10.1109/icdm.2005.22 · Cited by 55

Using novelty detection to identify abnormalities caused by mean shifts in bivariate processes

Zorriassatine, F., Tannock, J. D. T., & O'Brien, C. (2003) · Computers & Industrial Engineering · doi:10.1016/s0360-8352(02)00215-2 · Cited by 54

On-line novelty detection by recursive dynamic principal component analysis and gas sensor arrays under drift conditions

Perera-Lluna, A., Papamichail, N., Bârsan, N., Weimar, U., & Marco, S. (2006) · IEEE Sensors Journal · doi:10.1109/jsen.2006.874015 · Cited by 54

Graph-based text representation for novelty detection

Gamon, M. (2006) · doi:10.3115/1654758.1654762 · Cited by 53

Domain Anomaly Detection in Machine Perception: A System Architecture and Taxonomy

Kittler, J., Christmas, W. J., de Campos, T. E., Windridge, D., Yan, F., Illingworth, J., & Osman, M. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.209 · Cited by 53

Clustering Needles in a Haystack: An Information Theoretic Analysis of Minority and Outlier Detection

Ando, S. (2007) · Proceedings · doi:10.1109/icdm.2007.53 · Cited by 53

One-Class Classification with Gaussian Processes

Kemmler, M., Rodner, E., & Denzler, J. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-19309-5_38 · Cited by 51

Fault-diagnosis using neural networks with ellipsoidal basis functions

Jakubek, S., & Strasser, T. (2002) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.1109/acc.2002.1024528 · Cited by 51

The Novelty Detection Approach for Different Degrees of Class Imbalance

Lee, H., & Cho, S. (2006) · Lecture notes in computer science · doi:10.1007/11893257_3 · Cited by 51

RDF: A Density-Based Outlier Detection Method using Vertical Data Representation

Ren, D., Wang, B., & Perrizo, W. (2005) · Proceedings · doi:10.1109/icdm.2004.10010 · Cited by 49

HOT: hypergraph-based outlier test for categorical data

Li, W., Qian, W., Zhou, A., Jin, W., & Yu, J. X. (2003) · doi:10.5555/1760894.1760948 · Cited by 48

Improving the performance of radial basis function classifiers in condition monitoring and fault diagnosis applications where `unknown' faults may occur

Li, Y., Pont, M. J., & Jones, N. B. (2002) · Pattern Recognition Letters · doi:10.1016/s0167-8655(01)00133-7 · Cited by 48

A self-organizing neural network for detecting novelties

Albertini, M. K., & de Mello, R. F. (2007) · ACM Symposium on Applied Computing · doi:10.1145/1244002.1244110 · Cited by 47

Information sharing for distributed intrusion detection systems

Peng, T., Leckie, C. A., & Ramamohanarao, K. (2005) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2005.07.004 · Cited by 47

Review of novelty detection methods

Miljković, D. (2010) · International Convention on Information and Communication Technology, Electronics and Microelectronics · Cited by 47

Information theoretic novelty detection

Filippone, M., & Sanguinetti, G. (2009) · Pattern Recognition · doi:10.1016/j.patcog.2009.07.002 · Cited by 47

Non-Local Manifold Parzen Windows

Bengio, Y., Larochelle, H., & Vincent, P. (2005) · Cited by 47

An Anomaly Detection Algorithm for Detecting Attacks in Wireless Sensor Networks

Phuong, T. V., Hưng, L. X., Cho, S. J., Lee, Y.-K., & Lee, S. (2006) · Lecture notes in computer science · doi:10.1007/11760146_111 · Cited by 46

Choosing an appropriate model for novelty detection

Nairac, A. (1997) · doi:10.1049/cp:19970712 · Cited by 46

Novelty Detection Based Machine Health Prognostics

Filev, D., & Tseng, F. (2006) · doi:10.1109/isefs.2006.251161 · Cited by 46

Outlier detection with one-class kernel Fisher discriminants

Röth, V. (2005) · Cited by 46

The Gaussian Process Density Sampler

Murray, I., Mackay, D., & Adams, R. P. (2008) · neural information processing systems · Cited by 45

Modelling nonlinear count time series with local mixtures of Poisson autoregressions

de Carvalho, A. X. Y., & Tanner, M. A. (2006) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2006.09.032 · Cited by 44

A new anomaly detection method based on hierarchical HMM

Zhang, X., Fan, P., & Zhu, Z. (2004) · doi:10.1109/pdcat.2003.1236299 · Cited by 43

A multi-agent simulation system for prediction and scheduling of aero engine overhaul

Stranjak, A., Dutta, P. S., Ebden, M., Rogers, A., & Vytelingum, P. (2008) · ePrints Soton (University of Southampton) · doi:10.5555/1402795.1402811 · Cited by 42

Detecting outlying samples in microarray data: A critical assessment of the effect of outliers on sample classification

Kadota, K., Tominaga, D., Akiyama, Y., & Takahashi, K. (2003) · Chem-Bio Informatics Journal · doi:10.1273/cbij.3.30 · Cited by 42

An online adaptive condition-based maintenance method for mechanical systems

Wu, F., Wang, T., & Lee, J. (2010) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2010.04.003 · Cited by 41

Bayesian Extreme Value Statistics for Novelty Detection in Gas-Turbine Engines

Clifton, D. A., Tarassenko, L., McGrogan, N., King, D., King, S., & Anuzis, P. (2008) · IEEE Aerospace Conference (AERO) · doi:10.1109/aero.2008.4526423 · Cited by 41

Anytime online novelty and change detection for mobile robots

Sofman, B., Neuman, B., Stentz, A., & Bagnell, J. A. (2011) · Journal of Field Robotics · doi:10.1002/rob.20396 · Cited by 40

Bootstrapping a data mining intrusion detection system

Barbará, D., Li, Y., Couto, J., Lin, J., & Jajodia, S. (2003) · ACM Symposium on Applied Computing · doi:10.1145/952532.952616 · Cited by 39

On-line novelty detection using the Kalman filter and extreme value theory

Lee, H., & Roberts, S. (2008) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2008.4761918 · Cited by 39

Multi-sphere Support Vector Data Description for Outliers Detection on Multi-distribution Data

Xiao, Y., Liu, B., Cao, L., Wu, X., Zhang, C., Hao, Z., Yang, F., & Cao, J. (2009) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2009.87 · Cited by 39

Applying the Possibilistic c-Means Algorithm in Kernel-Induced Spaces

Filippone, M., Masulli, F., & Rovetta, S. (2010) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2010.2043440 · Cited by 38

Wildlife video key-frame extraction based on novelty detection in semantic context

Yong, S.-P., Deng, J. D., & Purvis, M. (2011) · Multimedia Tools and Applications · doi:10.1007/s11042-011-0902-2 · Cited by 37

An Extreme Function Theory for Novelty Detection

Clifton, D. A., Clifton, L., Hugueny, S., Wong, D., & Tarassenko, L. (2012) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2012.2234081 · Cited by 37

Feature extraction for novelty detection as applied to fault detection in machinery

McBain, J., & Timusk, M. A. (2011) · Pattern Recognition Letters · doi:10.1016/j.patrec.2011.01.019 · Cited by 37

High-speed inline defect detection for TFT-LCD array process using a novel support vector data description

Liu, Y., Liu, Y.-C., & Chen, Y.-Z. (2010) · Expert Systems with Applications · doi:10.1016/j.eswa.2010.11.046 · Cited by 37

A fast approach to novelty detection in video streams using recursive density estimation

Ramezani, R., Angelov, P. P., & Zhou, X. (2008) · International IEEE Conference on Intelligent Systems (IS) · doi:10.1109/is.2008.4670523 · Cited by 37

Outlier Detection Using Rough Set Theory

Jiang, F. X., Sui, Y., & Cao, C. (2005) · Lecture notes in computer science · doi:10.1007/11548706_9 · Cited by 35

Enabling the Discovery of Recurring Anomalies in Aerospace Problem Reports using High-Dimensional Clustering Techniques

Srivastava, A. N., Akella, R., Diev, V., Kumaresan, S. P., McIntosh, D. M. D., Pontikakis, E. D., Xu, Z., & Zhang, Y. (2006) · IEEE Aerospace Conference (AERO) · doi:10.1109/aero.2006.1656136 · Cited by 35

L1 norm based KPCA for novelty detection

Xiao, Y., Wang, H., Xu, W., & Zhou, J. (2012) · Pattern Recognition · doi:10.1016/j.patcog.2012.06.017 · Cited by 35

Unsupervised condition change detection in large diesel engines

Pontoppidan, N. H., & Larsen, J. O. (2003) · doi:10.1109/nnsp.2003.1318056 · Cited by 35

Known Unknowns: Novelty Detection in Condition Monitoring

Quinn, J. A., & Williams, C. K. I. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-72847-4_1 · Cited by 34

Ligand-Based Virtual Screening by Novelty Detection with Self-Organizing Maps

Hristozov, D., Oprea, T. I., & Gasteiger, J. (2007) · Journal of Chemical Information and Modeling · doi:10.1021/ci700040r · Cited by 34

Residual generation and visualization for understanding novel process conditions

Díaz, I., & Hollmén, J. (2003) · doi:10.1109/ijcnn.2002.1007460 · Cited by 33

Novelty detection in wildlife scenes through semantic context modelling

Yong, S.-P., Deng, J. D., & Purvis, M. (2012) · Pattern Recognition · doi:10.1016/j.patcog.2012.02.036 · Cited by 32

Real-time Automated Visual Inspection using Mobile Robots

Neto, H. V., & Nehmzow, U. (2007) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-007-9146-9 · Cited by 32

Autonomous Growing Neural Gas for applications with time constraint: Optimal parameter estimation

García‐Rodríguez, J., Angelopoulou, A., García‐Chamizo, J. M., Ψαρρού, Α., Orts‐Escolano, S., & Morell, V. (2012) · Neural Networks · doi:10.1016/j.neunet.2012.02.032 · Cited by 31

Incremental data-driven learning of a novelty detection model for one-class classification with application to high-dimensional noisy data

Kassab, R., & Alexandre, F. (2008) · Machine Learning · doi:10.1007/s10994-008-5092-4 · Cited by 31

Factor analysis based anomaly detection

Wu, N., & Zhang, J. (2004) · IEEE Systems, Man and Cybernetics Information Assurance Workshop (IAW) · doi:10.1109/smcsia.2003.1232408 · Cited by 31

Data Mining Based Network Intrusion Detection System: A Survey

Helali, R. G. M. (2009) · Springer eBooks · doi:10.1007/978-90-481-3662-9_86 · Cited by 30

A Framework for Novelty Detection in Jet Engine Vibration Data

Clifton, D. A., Bannister, P. R., & Tarassenko, L. (2007) · Key engineering materials · doi:10.4028/www.scientific.net/kem.347.305 · Cited by 30

Automated Novelty Detection in Industrial Systems

Clifton, D. A., Clifton, L., Bannister, P. R., & Tarassenko, L. (2008) · Studies in computational intelligence · doi:10.1007/978-3-540-78297-1_13 · Cited by 29

Gaussian mixture pdf in one-class classification: computing and utilizing confidence values

Ilonen, J., Paalanen, P., Kämäräinen, J., & Kalviainen, H. A. (2006) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2006.595 · Cited by 29

Factorial Switching Kalman Filters for Condition Monitoring in Neonatal Intensive Care

Williams, C. K. I., Quinn, J. A., & McIntosh, N. (2005) · ERA · Cited by 29

An optimal sphere and two large margins approach for novelty detection

Le, T., Tran, D., Ma, W., & Sharma, D. K. (2010) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2010.5596456 · Cited by 29

A tale of two filters-on-line novelty detection

Crook, P. A., Marsland, S., Hayes, G. R., & Nehmzow, U. (2003) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2002.1014330 · Cited by 28

Using Clustering and Robust Estimators to Detect Outliers in Multivariate Data.

Pires, A. M., & Pereira, C. S. (2005) · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) · Cited by 27

Novelty detection for practical pattern recognition in condition monitoring of multivariate processes: a case study

Zorriassatine, F., Al‐Habaibeh, A., Parkin, R. M., Jackson, M. R., & Coy, J. (2005) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-004-2174-8 · Cited by 26

Time Series Clustering for Anomaly Detection Using Competitive Neural Networks

Barreto, G. A., & Aguayo, L. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-02397-2_4 · Cited by 25

Support Vector Machine in Novelty Detection for Multi-channel Combustion Data

Clifton, L., Yin, H., & Zhang, Y. (2006) · Lecture notes in computer science · doi:10.1007/11760191_122 · Cited by 24

Efficient support vector data descriptions for novelty detection

Peng, X., & Xu, D. (2011) · Neural Computing and Applications · doi:10.1007/s00521-011-0625-3 · Cited by 24

Concepts for novelty detection and handling based on a case-based reasoning process scheme

Perner, P. (2008) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2008.05.003 · Cited by 24

HOT: Hypergraph-Based Outlier Test for Categorical Data

Li, W., Qian, W., Zhou, A., Jin, W., & Yu, J. X. (2003) · Lecture notes in computer science · doi:10.1007/3-540-36175-8_40 · Cited by 24

Application of LVQ to novelty detection using outlier training data

Lee, H., & Cho, S. (2006) · Pattern Recognition Letters · doi:10.1016/j.patrec.2006.02.019 · Cited by 23

fMRI analysis via one-class machine learning techniques

Hardoon, D. R., & Manevitz, L. M. (2005) · Cited by 22

Novelty Detection in Projected Spaces for Structural Health Monitoring

Toivola, J., Prada, M. A., & Hollmén, J. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-13062-5_20 · Cited by 20

Unsupervised learning of Gamma mixture models using Minimum Message Length

Agusta, Y., & Dowe, D. L. (2003) · Cited by 20

CD-Trees: An Efficient Index Structure for Outlier Detection

Sun, H., Bao, Y., Zhao, F., Yu, G., & Wang, D. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-27772-9_60 · Cited by 18

Understanding Categorical Similarity Measures for Outlier Detection

Chandola, V., Boriah, S., & Kumar, V. (2008) · University of Minnesota Digital Conservancy (University of Minnesota) · Cited by 17

Novelty Detection Based On Spectral Similarity Of Songs.

Flexer, A., Pampalk, E., & Widmer, G. (2005) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.1416503 · Cited by 16

Novelty detection using growing neural gas for visuo-spatial memory

Kit, D. M., Sullivan, B., & Ballard, D. (2011) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2011.6094794 · Cited by 15

Novelty detection by nonlinear factor analysis for structural health monitoring

Lämsä, V., & Raiko, T. (2010) · IEEE International Workshop on Machine Learning for Signal Processing (MLSP) · doi:10.1109/mlsp.2010.5588688 · Cited by 14

Real Time Novelty Detection Modeling for Machine Health Prognostics

Filev, D., & Tseng, F. (2006) · Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS) · doi:10.1109/nafips.2006.365465 · Cited by 13

Multivariate density estimation with optimal marginal parzen density estimation and gaussianization

Deniz Erdogmus, B., Jenssen, R., Rao, Y. N., & Príncipe, J. C. (2005) · Machine learning for signal processing ... · doi:10.1109/mlsp.2004.1422961 · Cited by 13

Unsupervised Outlier Detection and Semi-Supervised Learning ; CU-CS-976-04

Vinueza, A., & Grudić, G. Z. (2004) · CU Scholar (University of Colorado Boulder) · Cited by 13

Distance-Based Outliers in Sequences

Palshikar, G. K. (2005) · Lecture notes in computer science · doi:10.1007/11604655_61 · Cited by 12

On-line novelty detection for artefact identification in automatic anaesthesia record keeping

Hoare, S. W., Asbridge, D., & Beatty, P. C. W. (2002) · Medical Engineering & Physics · doi:10.1016/s1350-4533(02)00146-7 · Cited by 12

Combined Support Vector Novelty Detection for Multi-channel Combustion Data

Clifton, L., Yin, H., Clifton, D. A., & Zhang, Y. (2007) · IEEE International Conference on Networking, Sensing and Control (ICNSC) · doi:10.1109/icnsc.2007.372828 · Cited by 12

Learning Shape for Jet Engine Novelty Detection

Clifton, D. A., Bannister, P. R., & Tarassenko, L. (2006) · Lecture notes in computer science · doi:10.1007/11760191_121 · Cited by 11

Analysis of Time Series Novelty Detection Strategies for Synthetic and Real Data

Modenesi, A. P., & Braga, A. P. (2009) · Neural Processing Letters · doi:10.1007/s11063-009-9106-4 · Cited by 11

Identifying High-Risk Patients without Labeled Training Data: Anomaly Detection Methodologies to Predict Adverse Outcomes.

Syed, Z., Saeed, M., & Rubinfeld, I. S. (2010) · PubMed · Cited by 10

Novelty detection with multivariate Extreme Value Theory, part II: An analytical approach to unimodal estimation

Hugueny, S., Clifton, D. A., & Tarassenko, L. (2009) · IEEE International Workshop on Machine Learning for Signal Processing (MLSP) · doi:10.1109/mlsp.2009.5306228 · Cited by 10

Multiple Distribution Data Description Learning Algorithm for Novelty Detection

Le, T., Tran, D., Ma, W., & Sharma, D. K. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-20847-8_21 · Cited by 9

Pseudo-density Estimation for Clustering with Gaussian Processes

Kim, H., & Lee, J. (2006) · Lecture notes in computer science · doi:10.1007/11759966_183 · Cited by 9

Extreme value statistics for vibration spectra outlier detection

Hazan, A., Lacaille, J., & Madani, K. (2012) · HAL (Le Centre pour la Communication Scientifique Directe) · Cited by 8

A Perturbative Approach to Novelty Detection in Autoregressive Models

Filippone, M., & Sanguinetti, G. (2011) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2010.2094609 · Cited by 7

Aircraft engine health monitoring using density modelling and extreme value statistics

Sundaram, S. M., Igd., S., Clifton, D. A., Tarassenko, L., & King, S. P. (2009) · Cited by 7

Evolving a dynamic predictive coding mechanism for novelty detection

Haggett, S. J., Chu, D., & Marshall, I. W. (2007) · Knowledge-Based Systems · doi:10.1016/j.knosys.2007.11.007 · Cited by 6

A comparison of approaches to multivariate extreme value theory for novelty detection

Clifton, D. A., Hugueny, S., & Tarassenko, L. (2009) · IEEE/SP Workshop on Statistical Signal Processing (SSP) · doi:10.1109/ssp.2009.5278652 · Cited by 6

A surface representation approach for novelty detection

Li, Y. (2008) · International Conference on Information and Automation · doi:10.1109/icinfa.2008.4608233 · Cited by 6

New approaches based on One-Class SVMS for impulsive sounds recognition tasks

Rabaoui, A., Kadri, H., & Ellouze, N. (2008) · Machine learning for signal processing ... · doi:10.1109/mlsp.2008.4685494 · Cited by 5

Pinning the tail on the distribution: A multivariate extension to the generalised Pareto distribution

Clifton, D. A., Hugueny, S., & Tarassenko, L. (2011) · IEEE International Workshop on Machine Learning for Signal Processing (MLSP) · doi:10.1109/mlsp.2011.6064572 · Cited by 5

Detection of Dynamical Transitions in Biomedical Signals Using Nonlinear Methods

McSharry, P. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-30134-9_65 · Cited by 4

Novelty Detection Using Graphical Models for Semantic Room Classification

Pinto, A. S., Pronobis, A., & Reis, L. P. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-24769-9_24 · Cited by 4

A new online learning with Kernels method in novelty detection

Li, G., Wen, C., & Li, Z. G. (2011) · Proceedings of the Annual Conference of the IEEE Industrial Electronics Society · doi:10.1109/iecon.2011.6119670 · Cited by 4

Health status detection for patients in physiological monitoring

Chen, D., & Meng, M. Q. ‐. (2011) · doi:10.1109/iembs.2011.6091219 · Cited by 2

Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery

Schlegl, T., Seeböck, P., Waldstein, S. M., Schmidt-Erfurth, U., & Langs, G. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-59050-9_12 · Cited by 2,627

MVTec AD — A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection

Bergmann, P., Fauser, M., Sattlegger, D., & Steger, C. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00982 · Cited by 2,203

f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks

Schlegl, T., Seeböck, P., Waldstein, S. M., Langs, G., & Schmidt-Erfurth, U. M. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.01.010 · Cited by 1,645

A Unifying Review of Deep and Shallow Anomaly Detection

Ruff, L., Kauffmann, J., Vandermeulen, R. A., Montavon, G., Samek, W., Kloft, M., Dietterich, T. G., & Muller, K.-R. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3052449 · Cited by 1,184

Recent Advances in Open Set Recognition: A Survey

Geng, C., Huang, S.-J., & Chen, S. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.2981604 · Cited by 1,106

www.elsevier.com/locate/sigpro Noveltydetection: a review—part 1: statistical approaches

Markou, M., & Singh, S. (2003)

Novelty detection: a review—part 1: statistical approaches

Markou, M., & Singh, S. (2003) · Signal Processing · doi:10.1016/j.sigpro.2003.07.018 · Cited by 1,472

Novelty detection: a review—part 2:

Markou, M., & Singh, S. (2003) · Signal Processing · doi:10.1016/j.sigpro.2003.07.019 · Cited by 813

Review of novelty detection methods

Miljković, D. (2010) · International Convention on Information and Communication Technology, Electronics and Microelectronics · Cited by 47

Novelty Detection: A Review - Part 2: Neural network based approaches

Markou, M., & Singh, S. (2003)