A review of novelty detection

UCI Machine Learning Repository

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Variational autoencoders for anomalous jet tagging

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BINet: Multi-perspective business process anomaly classification

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Anomaly Detection Methods for Categorical Data

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Uncertainty quantification in drug design

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Burned Area Mapping in the Brazilian Savanna Using a One-Class Support Vector Machine Trained by Active Fires

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A novel double-hybrid learning method for modal frequency-based damage assessment of bridge structures under different environmental variation patterns

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Modal Strain Energy-based Structural Damage Identification: A Review and Comparative Study

Wang, S., & Xu, M. (2018) · Structural Engineering International · doi:10.1080/10168664.2018.1507607 · Cited by 92

Ultrasound-based identification of damage in wind turbine blades using novelty detection

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BINet: Multivariate Business Process Anomaly Detection Using Deep Learning

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Unsupervised learning-based damage assessment of full-scale civil structures under long-term and short-term monitoring

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Structural health monitoring by a novel probabilistic machine learning method based on extreme value theory and mixture quantile modeling

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A Combined Data-Driven and Model-Based Residual Selection Algorithm for Fault Detection and Isolation

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Clustering-based novelty detection for identification of non-technical losses

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Bayesian‐optimized unsupervised learning approach for structural damage detection

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Radio Frequency Fingerprinting on the Edge

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Gaussian Anomaly Detection by Modeling the Distribution of Normal Data in Pretrained Deep Features

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State-of-the-art on clustering data streams

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Zero-shot versus Many-shot: Unsupervised Texture Anomaly Detection

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Rethinking Reconstruction Autoencoder-Based Out-of-Distribution Detection

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Robust detection of unknown DoS/DDoS attacks in IoT networks using a hybrid learning model

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Deep Recurrent Neural Network-Based Autoencoders for Acoustic Novelty Detection

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Software engineering challenges for machine learning applications: A literature review

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Towards robust voice pathology detection

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Anomaly detection in mixed telemetry data using a sparse representation and dictionary learning

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Simulation-based Anomaly Detection and Damage Localization: An application to Structural Health Monitoring

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Deep Residual Flow for Out of Distribution Detection

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Denoising diffusion models for out-of-distribution detection

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Semi-Supervised Anomaly Detection with an Application to Water Analytics

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An Anomalous Noise Events Detector for Dynamic Road Traffic Noise Mapping in Real-Life Urban and Suburban Environments

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Novelty Detection using Deep Normative Modeling for IMU-Based Abnormal Movement Monitoring in Parkinson’s Disease and Autism Spectrum Disorders

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Hierarchical Novelty Detection for Visual Object Recognition

Lee, K., Lee, K., Min, K., Zhang, Y., Shin, J., & Lee, H. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00114 · Cited by 80

Learning in presence of class imbalance and class overlapping by using one-class SVM and undersampling technique

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Early warning of structural damage via manifold learning-aided data clustering and non-parametric probabilistic anomaly detection

Entezami, A., Sarmadi, H., Behkamal, B., & Mariani, S. (2024) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2024.111984 · Cited by 72

Using machine learning methods in airline flight data monitoring to generate new operational safety knowledge from existing data

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Learning Representations from Healthcare Time Series Data for Unsupervised Anomaly Detection

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Selection Features and Support Vector Machine for Credit Card Risk Identification

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Anomaly detection with variational quantum generative adversarial networks

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Systematization of Corner Cases for Visual Perception in Automated Driving

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Subspace Support Vector Data Description

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Analyzing rare event, anomaly, novelty and outlier detection terms under the supervised classification framework

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Detecting Out-of-Distribution Inputs to Deep Generative Models Using TypicalityPreprint

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A general anomaly detection framework for fleet-based condition monitoring of machines

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Machine learning for anomaly detection in particle physics

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Real-Time Nonparametric Anomaly Detection in High-Dimensional Settings

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Artificial intelligence and physics-based anomaly detection in the smart grid: A survey

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Automated identification of benthic epifauna with computer vision

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Unsupervised machine and deep learning methods for structural damage detection: A comparative study

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Gesture-Based Human–Machine Interaction: Taxonomy, Problem Definition, and Analysis

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CAE: Contextual auto-encoder for multivariate time-series anomaly detection in air transportation

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Non-parametric empirical machine learning for short-term and long-term structural health monitoring

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Minimal infrequent pattern based approach for mining outliers in data streams

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Robust methods of inclusive outlier analysis for structural health monitoring

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Detection of Anomalies in Industrial IoT Systems by Data Mining: Study of CHRIST Osmotron Water Purification System

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Machine learning-based anomaly detection via integration of manufacturing, inspection and after-sales service data

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OLE: Orthogonal Low-rank Embedding, A Plug and Play Geometric Loss for Deep Learning

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Anomaly detection with the Switching Kalman Filter for structural health monitoring

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Detection and identification of significant events in historical aircraft trajectory data

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A Systematic Literature Review on Outlier Detection in Wireless Sensor Networks

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Metric Learning for Novelty and Anomaly DetectionPreprint

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Towards a data-driven adaptive anomaly detection system for human activity

Yahaya, S., Lotfi, A., & Mahmud, M. (2021) · Pattern Recognition Letters · doi:10.1016/j.patrec.2021.02.006 · Cited by 44

Explainable machine learning in image classification models: An uncertainty quantification perspective

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Self-Attentive, Multi-Context One-Class Classification for Unsupervised Anomaly Detection on Text

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Recurrent Neural Radio Anomaly DetectionPreprint

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Unsupervised Anomaly Detection in Stream Data with Online Evolving Spiking Neural Networks

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Out-of-Distribution (OOD) Detection Based on Deep Learning: A Review

Cui, P., & Wang, J. (2022) · Electronics · doi:10.3390/electronics11213500 · Cited by 66

Sensor Fault Diagnosis for Structural Health Monitoring Based on Statistical Hypothesis Test and Missing Variable Approach

Huang, H., Yi, T., & Li, H. (2015) · Journal of Aerospace Engineering · doi:10.1061/(asce)as.1943-5525.0000572 · Cited by 61

Unsupervised Anomaly Detection in Noisy Business Process Event Logs Using Denoising Autoencoders

Nolle, T., Seeliger, A., & Mühlhäuser, M. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46307-0_28 · Cited by 61

Finding a ‘New’ Needle in the Haystack: Unseen Radio Detection in Large Populations Using Deep Learning

Gritsenko, A., Wang, Z., Jian, T., Dy, J., Chowdhury, K. R., & Ioannidis, S. (2019) · IEEE International Symposium on Dynamic Spectrum Access Networks · doi:10.1109/dyspan.2019.8935862 · Cited by 61

Detection of sub-surface damage in wind turbine bearings using acoustic emissions and probabilistic modelling

FUENTES, R., Dwyer-Joyce, R. S., Marshall, M., Wheals, J. C., & Cross, E. J. (2019) · Renewable Energy · doi:10.1016/j.renene.2019.08.019 · Cited by 60

Taxonomy of machine learning paradigms: A data‐centric perspective

Emmert‐Streib, F., & Dehmer, M. (2022) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1470 · Cited by 88

Specifics of medical data mining for diagnosis aid: A survey

Itani, S., Lecron, F., & Fortemps, P. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.09.056 · Cited by 62

A One-Class Generative Adversarial Detection Framework for Multifunctional Fault Diagnoses

Pu, Z., Cabrera, D. R., Bai, Y., & Li, C. (2021) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2021.3108719 · Cited by 61

A Visual Analytics Approach for Equipment Condition Monitoring in Smart Factories of Process Industry

Wu, W., Zheng, Y., Chen, K., Wang, X., & Cao, N. (2018) · IEEE Pacific Visualization Symposium · doi:10.1109/pacificvis.2018.00026 · Cited by 59

Image Anomalies: A Review and Synthesis of Detection Methods

Ehret, T., Davy, A., Morel, J., & Delbracio, M. (2019) · Journal of Mathematical Imaging and Vision · doi:10.1007/s10851-019-00885-0 · Cited by 59

MorphoCluster: Efficient Annotation of Plankton Images by Clustering

Schröder, S., Kiko, R., & Koch, R. (2020) · Sensors · doi:10.3390/s20113060 · Cited by 70

Fusion of Scores in a Detection Context Based on Alpha Integration

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Performance Evaluation of Techniques for Identifying Abnormal Energy Consumption in Buildings

Gaur, M., Makonin, S., Bajić, I. V., & Majumdar, A. (2019) · IEEE Access · doi:10.1109/access.2019.2915641 · Cited by 58

Statistical Anomaly Detection in Human Dynamics Monitoring Using a Hierarchical Dirichlet Process Hidden Markov Model

Fuse, T., & Kamiya, K. (2017) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2017.2674684 · Cited by 57

An Overview of Audio Event Detection Methods from Feature Extraction to Classification

Babaee, E., Anuar, N. B., Wahab, A. W. A., Shamshirband, S., & Chronopoulos, A. T. (2017) · Applied Artificial Intelligence · doi:10.1080/08839514.2018.1430469 · Cited by 57

Improving one class support vector machine novelty detection scheme using nonlinear features

Sadooghi, M. S., & Khadem, S. E. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.05.002 · Cited by 57

Hierarchical approach for uncertainty quantification and reliability assessment of guided wave-based structural health monitoring

Yue, N., & Aliabadi, M. H. (2020) · Structural Health Monitoring · doi:10.1177/1475921720940642 · Cited by 57

Obey validity limits of data-driven models through topological data analysis and one-class classification

Schweidtmann, A. M., Weber, J. M., Wende, C., Netze, L., & Mitsos, A. (2021) · Optimization and Engineering · doi:10.1007/s11081-021-09608-0 · Cited by 62

Modeling and recognition of smart grid faults by a combined approach of dissimilarity learning and one-class classification

Santis, E. D., Livi, L., Sadeghian, A., & Rizzi, A. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.05.112 · Cited by 60

Detecting anomalous structures by convolutional sparse models

Carrera, D., Boracchi, G., Foi, A., & Wohlberg, B. E. (2015) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2015.7280790 · Cited by 57

Learning deep structured network for weakly supervised change detection

Khan, S. H., He, X., Porikli, F., Bennamoun, M., Sohel, F. A., & Togneri, R. B. (2017) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2017/279 · Cited by 57

Acoustic novelty detection with adversarial autoencoders

Principi, E., Vesperini, F., Squartini, S., & Piazza, F. (2017) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2017.7966273 · Cited by 60

A Systematic Review of Anomaly Detection for Business Process Event Logs

Ko, J., & Comuzzi, M. (2023) · Business & Information Systems Engineering · doi:10.1007/s12599-023-00794-y · Cited by 56

An unsupervised machine learning approach for real-time damage detection in bridges

Bayane, I., Leander, J., & Karoumi, R. (2024) · Engineering Structures · doi:10.1016/j.engstruct.2024.117971 · Cited by 63

Novelty-focused weak signal detection in futuristic data: Assessing the rarity and paradigm unrelatedness of signals

Kim, J., & Lee, C. (2017) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2017.04.006 · Cited by 62

Open Set Recognition by Regularising Classifier with Fake Data Generated by Generative Adversarial Networks

Jo, I., Kim, J., Kang, H., Kim, Y., & Choi, S. (2018) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2018.8461700 · Cited by 56

Combination of research questions and methods: A new measurement of scientific novelty

Luo, Z., Lu, W. P., He, J., & Wang, Y. (2022) · Journal of Informetrics · doi:10.1016/j.joi.2022.101282 · Cited by 65

Local Novelty Detection in Multi-class Recognition Problems

Bodesheim, P., Freytag, A., Rodner, E., & Denzler, J. (2015) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2015.113 · Cited by 53

Incremental Kernel Null Space Discriminant Analysis for Novelty Detection

Liu, J., Lian, Z., Wang, Y., & Xiao, J. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.439 · Cited by 55

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

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