Tutorial “ Change and Anomaly Detection in Signals, Images, and General Data Streams ”

Presenter : Giacomo Boracchi, Politecnico di Milano, DEIB

Change and anomaly detection problems are ubiquitous in engineering. The prompt detection of changes and anomalies is often a primary concern, as they provide precious information for understanding the dynamics of a monitored process, and for activating suitable countermeasures. Changes, for instance, might indicate an unforeseen evolution of the process generating the data, or a fault in a machinery. Anomalies are typically considered the most informative samples, as for instance arrhythmias in an ECG tracing or frauds in a stream of credit card transactions. Not surprisingly, detection problems in time series/images/videos have been widely investigated in the signal processing community, in application scenarios that range from quality inspection to health monitoring.

Aims and scope
The tutorial presents a rigorous formulation of the change and anomaly detection problems, which fits many signal/image analysis techniques and applications, including sequential monitoring and detection by classification. The tutorial describes in detail the most important approaches in the literature, following the machine-learning perspective of supervised, semi-supervised and unsupervised monitoring tasks. Particular emphasis will be given to: i) issues raising in multivariate settings, where the popular approach of monitoring the log-likelihood will be demonstrated to loose power when data-dimension increases, and ii) change/anomaly detection methods that use learned models, which are often adopted to handle signals and images. The tutorial also illustrates how advanced learned models, like convolutional sparse representation and structured dictionaries, as well as domain-adaptation techniques, can be used to enhance detection algorithms. Finally, best practices for designing suitable experimental testbed will be discussed.
The tutorial is accompanied by various examples where change/anomaly detection algorithms are applied to solve real world problems. These include ECG monitoring in wearable devices, image analysis to detect defects in industrial manufacturing, and fraud detection in credit card transactions


ICASSP 2018 Tutorial: Anomaly/Change Detection in The Random Variable World
Giacomo Boracchi Tutorial at IEEE ICASSP 2018;
April 16 2018, Calgary, Canada
(Slides -- part 1);

ICASSP 2018 Tutorial: Anomaly/Change Detection Out of the Random Variable World
Giacomo Boracchi Tutorial at IEEE ICASSP 2018;
April 16 2018, Calgary, Canada
(Slides -- part 2);

IJCNN 2017 Tutorial: Change and Anomaly Detection in Data Streams
Giacomo Boracchi Tutorial at IJCNN 2017 The INNS/IEEE International Joint Conference on Neural Networks;
May 14-19, 2017, Anchorage, Alaska, USA
(Slides), (Old Tutorial Website);

INNS BigData 2016 Tutorial: Change Detection in Data Streams: Big Data Challenges
Giacomo Boracchi Tutorial at INNS Conference on Big Data;
October 23rd - 25th, 2016, Thessaloniki, Grece
(Abstract); (Slides);


[Boracchi et al. 2018] QuantTree: Histograms for Change Detection in Multivariate Data Streams
Giacomo Boracchi, Diego Carrera, Cristiano Cervellera, Danilo Maccio'
, Intenational Conference on Machine Learning (ICML) 2018 -- Accepted, 8 pages, 2018
(Preprint), (Source Code)

[Alippi et al. 2016] Change Detection in Multivariate Datastreams: Likelihood and Detectability Loss
Cesare Alippi, Giacomo Boracchi, Diego Carrera, Manuel Roveri
, International Joint Conference of Artificial Intelligence (IJCAI) 2016, New York, USA, July 9 - 13
(Preprint), (Original), (BibTeX).

[Carrera et al. 2016 a] ECG Monitoring in Wearable Devices by Sparse Models
Diego Carrera, Beatrice Rossi, Daniele Zambon, Pasqualina Fragneto, and Giacomo Boracchi
, Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery, ECML-PKDD 2016, Riva del Garda, Italy, September 19 - 23, Accepted, 16 pages

[Carrera et al. 2016 b] Defect Detection in SEM Images of Nanofibrous Materials
Diego Carrera, Fabio Manganini, Giacomo Boracchi, Ettore Lanzarone
, IEEE Transactions on Industrial Informatics -- In Press, 11 pages, doi:10.1109/TII.2016.2641472
(Preprint), (Original), (Dataset),

[Carrera et al. 2016 c] CCM: Controlling the Change Magnitude in High Dimensional Data
Cesare Alippi, Giacomo Boracchi, Diego Carrera
, Proceedings of INNS Conference on Big Data, 2016 , Thessaloniki, Greece, October 23 - 25, 2016, 10 pages
(Preprint), (Slides) (Codes).
This paper has received the Best Regular Paper Award.

[Dal Pozzolo et al. 2015] Credit Card Fraud Detection and Concept-Drift Adaptation with Delayed Supervised Information
Andrea Dal Pozzolo, Giacomo Boracchi, Olivier Caelen, Cesare Alippi and Gianluca Bontempi
, Proceedings of International Joint Conference on Neural Networks IJCNN 2015, Killarney, Irland, July 12 - 17,
(Preprint), (BibTeX), (Original).

[Boracchi and Roveri. 2014] Exploiting Self-Similarity for Change Detection
Giacomo Boracchi, Manuel Roveri
IJCNN 2014 International Joint Conference on Neural Networks, Beijing, China July 6 - 11,

[Alippi et al. 2011 a] A Hierarchical, Nonparametric Sequential Change-Detection Test
Cesare Alippi, Giacomo Boracchi and Manuel Roveri,
in Proceedings of IJCNN 2011, the International Joint Conference on Neural Networks, San Jose, California July 31 - August 5, 2011. pp 2889 - 2896, doi: 10.1109/IJCNN.2011.6033600
(Preprint), (BibTeX), (Original)

[Alippi et al. 2011 b] A just-in-time adaptive classification system based on the intersection of confidence intervals rule,
Cesare Alippi, Giacomo Boracchi, Manuel Roveri
Neural Networks, Elsevier vol. 24 (2011), pp. 791-800
doi: 10.1016/j.neunet.2011.05.012
(Preprint), (BibTeX), (Original)