A review of novelty detection

UCI Machine Learning Repository

Asuncion, A. (2007) · Medical Entomology and Zoology · Cited by 24,190

Book Review: Pattern Recognition and Machine Learning

Nasrabadi, N. M. (2007) · Journal of Electronic Imaging · doi:10.1117/1.2819119 · Cited by 22,066

Pattern Classification

Seel, N. M. (2012) · Springer eBooks · doi:10.1007/978-1-4419-1428-6_5195 · Cited by 19,482

Anomaly detection: A survey

Chandola, V., Banerjee, A., & Kumar, V. (2009) · ACM Computing Surveys · doi:10.1145/1541880.1541882 · Cited by 11,687

Fast algorithms for mining association rules

Agrawal, R., & Srikant, R. (1998) · Cited by 11,362

On Estimation of a Probability Density Function and Mode

Parzen, E. (1962) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177704472 · Cited by 10,715

The self-organizing map

Kohonen, T. (1990) · Neurocomputing · doi:10.1016/s0925-2312(98)00030-7 · Cited by 10,686

Learning from Imbalanced Data

He, H., & Garcia, E. A. (2009) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2008.239 · Cited by 10,359

Pattern Recognition with Fuzzy Objective Function Algorithms

Bezdek, J. C. (1981) · doi:10.1007/978-1-4757-0450-1 · Cited by 9,881

Fast Algorithms for Mining Association Rules in Large Databases

Agrawal, R., & Srikant, R. (1994) · Very Large Data Bases · Cited by 9,454

Novelty Detection in Time Series Using Self-Organizing Neural Networks: A Comprehensive Evaluation

Aguayo, L., & Barreto, G. A. (2017) · Neural Processing Letters · doi:10.1007/s11063-017-9679-2 · Cited by 18

Clustering-based novelty detection to uncover electricity theft

Viegas, J. L., & Vieira, S. M. (2017) · IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) · doi:10.1109/fuzz-ieee.2017.8015546 · Cited by 18

A clustering-based deep autoencoder for one-class image classification

Gutoski, M., Ribeiro, M., Romero, M., Lazzaretti, A. E., & Lopes, H. S. (2017) · IEEE Latin American Conference on Computational Intelligence (LA-CCI) · doi:10.1109/la-cci.2017.8285680 · Cited by 19

Application of new training methods for neural model reference control

Jafari, A. H., & Hagan, M. (2018) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2018.07.005 · Cited by 18

Non-convex hull based anomaly detection in CPPS

Li, P., & Niggemann, O. (2019) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2019.103301 · Cited by 18

Extended Autoencoder for Novelty Detection with Reconstruction along Projection Pathway

Shin, S. Y., & Kim, H. (2020) · Applied Sciences · doi:10.3390/app10134497 · Cited by 18

Risk, Trust, and Bias: Causal Regulators of Biometric-Enabled Decision Support

Lai, K., Oliveira, H. C. R., Hou, M., Yanushkevich, S. N., & Shmerko, V. P. (2020) · IEEE Access · doi:10.1109/access.2020.3015855 · Cited by 18

An anomaly detection method for rotating machinery monitoring based on the most representative data

Lis, A., Dworakowski, Z., & Czubak, P. (2021) · Journal of Vibroengineering · doi:10.21595/jve.2021.21622 · Cited by 18

Custom Outlier Detection for Electrical Energy Consumption Data Applied in Case of Demand Response in Block of Buildings

Jurj, D. I., Czumbil, L., Bârgăuan, B., Ceclan, A., Polycarpou, A., & Micu, D. D. (2021) · Sensors · doi:10.3390/s21092946 · Cited by 18

Deep-Compact-Clustering Based Anomaly Detection Applied to Electromechanical Industrial Systems

Arellano-Espitia, F., Delgado-Prieto, M., Gonzalez-Abreu, A.-D., Saucedo-Dorantes, J. J., & Osornio-Rios, R. A. (2021) · Sensors · doi:10.3390/s21175830 · Cited by 19

CPDGA: Change point driven growing auto-encoder for lifelong anomaly detection

Corizzo, R., Baron, M. I., & Japkowicz, N. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.108756 · Cited by 18

Outlier analysis for accelerating clinical discovery: An augmented intelligence framework and a systematic review

Janoudi, G., Uzun, M., Fell, D. B., Ray, J. G., Foster, A. M., Giffen, R., Clifford, T., & Walker, M. C. (2024) · PLOS Digital Health · doi:10.1371/journal.pdig.0000515 · Cited by 18

Molecular deep learning at the edge of chemical space

van Tilborg, D., Rossen, L., & Grisoni, F. (2026) · Nature Machine Intelligence · doi:10.1038/s42256-026-01216-w · Cited by 24

Overcoming Blind Spots in the Real World: Leveraging Complementary Abilities for Joint Execution

Ramakrishnan, R., Kamar, E. S., Nushi, B., Dey, D., Shah, J., & Horvitz, E. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33016137 · Cited by 17

Learning the representation of raw acoustic emission signals by direct generative modelling and its use in chronology-based clusters identification

Ramasso, E., Butaud, P., Jeannin, T., Fabrizio Sarasini, A., Placet, V., Godin, N., Tirillò, J., & Gabrion, X. (2020) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2020.103478 · Cited by 17

Real-time event detection and classification in social text steam using embedding

Singh, T., Kumari, M., & Gupta, D. S. (2022) · Cluster Computing · doi:10.1007/s10586-022-03610-6 · Cited by 18

Mutual information maximization for semi-supervised anomaly detection

Liu, S., & Tian, M. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.111196 · Cited by 17

Active Learning for Data Quality Control: A Survey

Li, N., Qi, Y.-Y., Li, C.-R., & Zhao, Z.-M. (2024) · ACM Journal of Data and Information Quality · doi:10.1145/3663369 · Cited by 15

Novelty detection with CANDIES: a holistic technique based on probabilistic models

Gruhl, C., & Sick, B. (2016) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-016-0618-8 · Cited by 17

Fault Diagnosis and Detection

Demetgül, M., & Ünal, M. (2017) · InTech eBooks · doi:10.5772/63169 · Cited by 17

Anomaly Detection in Multivariate Non-stationary Time Series for Automatic DBMS Diagnosis

Lee, D. (2017) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2017.0-126 · Cited by 17

Random distortion testing with linear measurements

Pastor, D., & Socheleau, F.-X. (2017) · Signal Processing · doi:10.1016/j.sigpro.2017.11.017 · Cited by 17

An Incremental Local Outlier Detection Method in the Data Stream

Yao, H., Fu, X., Yang, Y., & Postolache, O. A. (2018) · Applied Sciences · doi:10.3390/app8081248 · Cited by 17

Improving novelty detection with generative adversarial networks on hand gesture data

Simao, M. A., Neto, P., & Gibaru, O. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.05.064 · Cited by 17

Online Amnestic DTW to allow Real-Time Golden Batch Monitoring

Yeh, C. M., Zhu, Y., Dau, H. A., Darvishzadeh, A., Noskov, M. V., & Keogh, E. (2019) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3292500.3330650 · Cited by 17

A framework to integrate novelty detection and remaining useful life prediction in Industry 4.0-based manufacturing systems

Cattaneo, L., Polenghi, A., & Macchi, M. (2021) · International Journal of Computer Integrated Manufacturing · doi:10.1080/0951192x.2021.1885062 · Cited by 17

Real-time novelty detection of an industrial gas turbine using performance deviation model and extreme function theory

Gu, X., Yang, S., Sui, Y., Papatheou, E., Ball, A. D., & Gu, F. (2021) · Measurement · doi:10.1016/j.measurement.2021.109339 · Cited by 17

A New Ensemble Method for Detecting Anomalies in Gene Expression Matrices

Selicato, L., Esposito, F., Gargano, G., Vegliante, M. C., Opinto, G., Zaccaria, G. M., Ciavarella, S., Guarini, A., & Buono, N. D. (2021) · Mathematics · doi:10.3390/math9080882 · Cited by 17

Global Plus Local Jointly Regularized Support Vector Data Description for Novelty Detection

Hu, W., Hu, T., Wei, Y., Lou, J., & Wang, S. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3129321 · Cited by 17

Little data is often enough for distance-based outlier detection

Muhr, D., & Affenzeller, M. (2022) · Procedia Computer Science · doi:10.1016/j.procs.2022.01.297 · Cited by 17

An Efficient System for Stock Market Prediction

Hussein, A. S., Hamed, I. M., & Tolba, M. F. (2014) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-11310-4_76 · Cited by 17

Online personal risk detection based on behavioural and physiological patterns

Barrera-Animas, A. Y., Trejo, L. A., Medina‐Pérez, M. A., Monroy, R., Camiña, J. B., & Godínez, F. (2016) · Information Sciences · doi:10.1016/j.ins.2016.08.006 · Cited by 17

Vibration based diagnostic of cracks in hybrid ball bearings

Seimert, M., & Gühmann, C. (2017) · Measurement · doi:10.1016/j.measurement.2017.03.001 · Cited by 17

Robust One-Class Kernel Spectral Regression

Arashloo, S. R., & Kittler, J. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2979823 · Cited by 17

Frequentist uncertainty estimates for deep learning.Preprint

Tagasovska, N., & López-Paz, D. (2018) · arXiv (Cornell University) · Cited by 17

Implementing Anomaly-Based Intrusion Detection for Resource-Constrained Devices in IoMT Networks

Zachos, G., Μαντάς, Γ., Porfyrakis, K., & Rodrı́guez, J. (2025) · Sensors · doi:10.3390/s25041216 · Cited by 16

Phenotypic characterisation of Crohn's disease severity

Niehaus, K. E., Uhlig, H. H., & Clifton, D. A. (2015) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2015.7320009 · Cited by 16

Industrial Platform for Rapid Prototyping of Intelligent Diagnostic Systems

Żabiński, T., Mączka, T., & Kluska, J. (2017) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-60699-6_69 · Cited by 16

A fusion approach to improve accuracy and estimate uncertainty in cuffless blood pressure monitoring

Landry, C., Peterson, S. D., & Arami, A. (2022) · Scientific Reports · doi:10.1038/s41598-022-12087-7 · Cited by 16

Endowing data-driven models with rejection ability: Out-of-distribution detection and confidence estimation for black-box models of building energy systems

Liang, X., Zhu, X., Chen, K., Chen, S., Jin, X., & Du, Z. (2022) · Energy · doi:10.1016/j.energy.2022.125858 · Cited by 15

Anomaly Awareness

Khosa, C. K., & Veronica Sanz, B. (2023) · SciPost Physics · doi:10.21468/scipostphys.15.2.053 · Cited by 44

A Deep-LSTM-Based Fault Detection Method for Railway Vehicle Suspensions

Chen, Y., Liu, X., Fan, W., Duan, N., & Zhou, K. (2024) · Machines · doi:10.3390/machines12020116 · Cited by 17

Novelty-aware concept drift detection for neural networks

Shang, D., Zhang, G., & Jie Lu, B. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128933 · Cited by 16

One class classification applied in facial image analysis

Mygdalis, V., Iosifidis, A., Tefas, A., & Pitas, I. (2016) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2016.7532637 · Cited by 16

Crowdsourcing Biodiversity Monitoring

Joly, A., Goëau, H., Champ, J., Dufour-Kowalski, S., Müller, H., & Bonnet, P. (2016) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2964284.2976762 · Cited by 18

Constraints and Adaptation of Closed-Loop Neuroprosthetics for Functional Restoration

Bauer, R. H., & Gharabaghi, A. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00111 · Cited by 16

Categorized Representations and General Learning

Dolgikh, S. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-35249-3_11 · Cited by 19

Machine Learning for Cyber Physical Systems

Beyerer, J., Maier, A., & Niggemann, O. (2020) · Technologien für die intelligente Automation · doi:10.1007/978-3-662-62746-4 · Cited by 15

Visual Exploration of Anomalies in Cyclic Time Series Data with Matrix and Glyph Representations

Suschnigg, J., Mutlu, B., Koutroulis, G., Sabol, V., Thalmann, S., & Schreck, T. (2021) · Big Data Research · doi:10.1016/j.bdr.2021.100251 · Cited by 16

Vibration Monitoring of Gas Turbine Engines: Machine-Learning Approaches and Their Challenges

Matthaiou, I., Khandelwal, B., & Antoniadou, I. (2017) · Frontiers in Built Environment · doi:10.3389/fbuil.2017.00054 · Cited by 16

EMG and IMU based real-time HCI using dynamic hand gestures for a multiple-DoF robot arm

Shin, S., Tafreshi, R., & Langari, R. (2018) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-171562 · Cited by 16

Data Discovery and Anomaly Detection Using Atypicality for Real-Valued Data

Sabeti, E., & Høst-Madsen, A. (2019) · Entropy · doi:10.3390/e21030219 · Cited by 16

Adversarially learned one-class novelty detection with confidence estimation

Zhang, Y., Zhou, B., Ding, X., Ouyang, J., Cai, X., Gao, J., & Yuan, X. (2020) · Information Sciences · doi:10.1016/j.ins.2020.11.052 · Cited by 16

Deep learning models for predictive maintenance: a survey, comparison, challenges and prospectPreprint

Serradilla, O., Zugasti, E., & Zurutuza, U. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.03207 · Cited by 16

Anomalous Sound Detection as a Simple Binary Classification Problem with Careful Selection of Proxy Outlier ExamplesPreprint

Primus, P., Haunschmid, V., Praher, P., & Widmer, G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.02949 · Cited by 16

Multi-Source Data Integration and Model Coupling for Watershed Eco-Assessment Systems: Progress, Challenges, and Prospects

Ma, L., Xu, Z., Fan, L., Jia, H., Hu, H., & Li, L. (2025) · Processes · doi:10.3390/pr13092998 · Cited by 15

Anomaly-Aware Traffic Prediction Based on Automated Conditional Information Fusion

Sun, B., Cheng, W., Ma, L., & Goswami, P. (2018) · International Conference on Information Fusion (FUSION) · doi:10.23919/icif.2018.8455244 · Cited by 15

Clustering ensemble-based novelty score for outlier detection

Yu, J., & Kang, J. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106164 · Cited by 15

Kullback-Leibler Divergence-Based Out-of-Distribution Detection With Flow-Based Generative Models

Zhang, Y., Pan, J., Liu, W., Chen, Z., Li, K., Wang, J., Liu, Z., & Wei, H. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3309853 · Cited by 23

A survey of anomaly detection techniques

Ghamry, F. M., El‐Banby, G. M., El‐Fishawy, A. S., El‐Samie, F. E. A., & Dessouky, M. I. (2024) · Journal of Optics · doi:10.1007/s12596-023-01147-4 · Cited by 19

Universal digital high-resolution melting for the detection of pulmonary mold infections

Goshia, T., Aralar, A., Wiederhold, N. P., Jenks, J. D., Mehta, S. R., Karmakar, A., Monish, E. S., Sharma, A., Sun, H., Kebadireng, R., et al. (2024) · Journal of Clinical Microbiology · doi:10.1128/jcm.01476-23 · Cited by 18

Ensemble One-Class Support Vector Machine for Sea Surface Target Detection Based on k-Means Clustering

Chen, S., Ouyang, X., & Luo, F. (2024) · Remote Sensing · doi:10.3390/rs16132401 · Cited by 15

Unsupervised learning and pattern recognition in alloy design

Bhat, N., Birbilis, N., & Barnard, A. S. (2024) · Digital Discovery · doi:10.1039/d4dd00282b · Cited by 18

Reasoning and Knowledge Acquisition Framework for 5G Network Analytics

Monge, M. A. S., Vidal, J. M., & Villalba, L. J. G. (2017) · Sensors · doi:10.3390/s17102405 · Cited by 15

Unsupervised Abnormal Sensor Signal Detection With Channelwise Reconstruction Errors

Kwak, M. G., & Kim, S. B. (2021) · IEEE Access · doi:10.1109/access.2021.3064563 · Cited by 15

DDR-ID: dual deep reconstruction networks based image decomposition for anomaly detection

Lin, D., Li, Y., Xie, S., Nwe, T. L., & Dong, S. (2021) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-021-03425-0 · Cited by 15

Human Activity Recognition Machine With an Anchor-Based Loss Function

Jin, L., Wang, X., Chu, J., & He, M. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3130761 · Cited by 15

Unsupervised rail vehicle running instability detection algorithm for passenger trains (iVRIDA)

Kulkarni, R., Qazizadeh, A., & Berg, M. (2023) · Measurement · doi:10.1016/j.measurement.2023.112894 · Cited by 15

Estimating p-Values for Deviation Detection

Rognvaldsson, T. S., Norrman, H., Byttner, S., & Järpe, E. (2014) · IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO) · doi:10.1109/saso.2014.22 · Cited by 15

Machine Learning for Healthcare Technologies

(2016) · Institution of Engineering and Technology eBooks · doi:10.1049/pbhe002e · Cited by 15

Infinite Bayesian one-class support vector machine based on Dirichlet process mixture clustering

Zhang, W., Du, L., Li, L., Zhang, X., & Liu, H. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.01.006 · Cited by 15

Analysis of Customer Reviews for Product Service System Design based on Cloud Computing

Chen, D., Zhang, D., Tao, F., & Liu, A. (2019) · Procedia CIRP · doi:10.1016/j.procir.2019.03.116 · Cited by 19

Discriminative Autoencoding Framework for Simple and Efficient Anomaly Detection

Mao, S., Guo, J., & Li, Z. (2019) · IEEE Access · doi:10.1109/access.2019.2933602 · Cited by 16

Anomaly Detection in ImagesPreprint

Minhas, M. S., & Zelek, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.13147 · Cited by 15

Semi-supervised and Unsupervised Methods for Heart Sounds Classification in Restricted Data EnvironmentsPreprint

Unnikrishnan, B., Singh, P. R., Yang, X., & Chua, M. C. H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.02610 · Cited by 16

Early-warning of ARDS using novelty detection and data fusion

Taoum, A., Mourad-chehade, F., & Amoud, H. (2018) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2018.09.030 · Cited by 14

A Survey on Video Anomaly Detection

Yadav, R. K., & Kumar, R. (2022) · IEEE Delhi Section Conference (DELCON) · doi:10.1109/delcon54057.2022.9753580 · Cited by 14

Federated Multi-Discriminator BiWGAN-GP Based Collaborative Anomaly Detection for Virtualized Network Slicing

Wang, W., Liang, C., Tang, L., Yanıkömeroğlu, H., & Chen, Q. (2022) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2022.3200059 · Cited by 14

Novelty Detection in Network Traffic: Using Survival Analysis for Feature Identification

Bradley, T., Alhajjar, E., & Bastian, N. D. (2023) · IEEE International Conference on Assured Autonomy (ICAA) · doi:10.1109/icaa58325.2023.00010 · Cited by 14

Malicious traffic detection on sampled network flow data with novelty-detection-based models

Campazas, A., Crespo-Martínez, I. S., Guerrero‐Higueras, Á. M., Álvarez-Aparicio, C., Olivera, V. M., & Fernández, C. (2023) · Scientific Reports · doi:10.1038/s41598-023-42618-9 · Cited by 14

Unsupervised 3D Out-of-Distribution Detection with Latent Diffusion Models

Graham, M. S., Pinaya, W. H. L., Wright, P. K., Tudosiu, P.-D., Mah, Y., Teo, J., Jäger, H. R., Werring, D. J., Nachev, P., Sebastien Ourselin, A., et al. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43907-0_43 · Cited by 14

A Survey of Change Point Detection in Dynamic Graphs

Zhou, Y., Gao, S. B., Guo, D., Wei, X., Rokne, J. G., & Wang, H. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3523857 · Cited by 17

Large-Scale Classification by an Approximate Least Squares One-Class Support Vector Machine Ensemble

Mygdalis, V., Iosifidis, A., Tefas, A., & Pitas, I. (2015) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom.2015.555 · Cited by 14

N2DLOF: A New Local Density-Based Outlier Detection Approach for Scattered Data

Su, S., Xiao, L., Zhang, Z., Gu, F., Ruan, L., Li, S., He, Z., Huo, Z., Yan, B., Wang, H., et al. (2017) · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/hpcc-smartcity-dss.2017.60 · Cited by 13

Unknown Landscape Identification with CNN Transfer Learning

Serra, E., Sharma, A., Joaristi, M., & Korzh, O. (2018) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1109/asonam.2018.8508357 · Cited by 14

Zero-shot learning with Multi-Battery Factor Analysis

Ji, Z., Xie, Y., Pang, Y., Chen, L., & Zhang, Z. M. (2017) · Signal Processing · doi:10.1016/j.sigpro.2017.03.023 · Cited by 14

Online distributed distance-based outlier clearance approaches for wireless sensor networks

Dai, T., & Ding, Z. (2020) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2020.101130 · Cited by 14

Unsupervised modeling anomaly detection in discussion forums posts using global vectors for text representation

Cichosz, P. (2020) · Natural Language Engineering · doi:10.1017/s1351324920000066 · Cited by 15

Integrating Predictive Maintenance in Adaptive Process Scheduling for a Safe and Efficient Industrial Process

Görür, O. C., Yu, X. Y., & Sivrikaya, F. (2021) · Applied Sciences · doi:10.3390/app11115042 · Cited by 18

On the adoption of abductive reasoning for time series interpretation

Teijeiro, T., & Félix, P. (2018) · Artificial Intelligence · doi:10.1016/j.artint.2018.06.005 · Cited by 14

Background Check: A General Technique to Build More Reliable and Versatile Classifiers

Perelló-Nieto, M., Filho, T. M. S., Kull, M., & Flach, P. A. (2016) · Proceedings · doi:10.1109/icdm.2016.0150 · Cited by 14

Gaussian Processes for Personalized Interpretable Volatility Metrics in the Step-Down Ward

Colopy, G. W., Roberts, S., & Clifton, D. A. (2019) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2019.2890823 · Cited by 14

Improving the Performance of Gas Sensor Systems with Advanced Data Evaluation, Operation, and Calibration Methods

Bastuck, M. (2019) · Linköping studies in science and technology. Dissertations · doi:10.3384/diss.diva-159106 · Cited by 14

Novel device detection using RF fingerprints

Robinson, J., & Kuzdeba, S. (2021) · IEEE Annual Computing and Communication Workshop and Conference (CCWC) · doi:10.1109/ccwc51732.2021.9376072 · Cited by 16

Self-adversarial Variational Autoencoder with Gaussian Anomaly Prior Distribution for Anomaly Detection.Preprint

Wang, X., Du, Y., Lin, S., Cui, P. Y., & Yang, Y. (2019) · arXiv (Cornell University) · Cited by 14

Quantifying Aleatoric and Epistemic Uncertainty Using Density Estimation in Latent Space.Preprint

Postels, J., Blum, H., Cadena, C., Siegwart, R., Gool, L. V., & Tombari, F. (2020) · arXiv (Cornell University) · Cited by 15

On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis ApproachPreprint

Aburakhia, S., Shami, A. A., & Karagiannidis, G. K. (2024) · doi:10.36227/techrxiv.171177312.27398605/v1 · Cited by 19

Leveraging Vector-Quantized Variational Autoencoder Inner Metrics for Anomaly Detection

Gangloff, H., Pham, M., Courtrai, L., & Lefèvre, S. (2022) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr56361.2022.9956102 · Cited by 13

Global and Multiscale Aggregate Network for Saliency Object Detection in Optical Remote Sensing Images

Huo, L., Hou, J., Feng, J., Wang, W., & Liu, J. (2024) · Remote Sensing · doi:10.3390/rs16040624 · Cited by 13

A methodology for monitoring smart buildings with incomplete models

Khorasgani, H., & Biswas, G. (2018) · Applied Soft Computing · doi:10.1016/j.asoc.2018.06.018 · Cited by 13

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)