Analytical Techniques for Anomaly Detection Through Features, Signal-Noise Separation and Partial-Value Association

Nong Ye
Proceedings of the KDD 2017: Workshop on Anomaly Detection in Finance, PMLR 71:20-32, 2018.

Abstract

This paper presents three analytical techniques for anomaly detection which can play an important role for anomaly detection in finance: the feature extraction technique, the signal-noise separation technique, and the Partial-Value Association Discovery (PVAD) algorithm. The feature extraction technique emphasizes the importance of extracting various data features which may be better at separating anomalies from norms than using raw data. The signal-noise separation technique considers an anomaly as the signal to detect and the norm as the noise and employs both anomaly models and norm models to detect anomalies accurately. The PVAD algorithm enables learning from data to build anomaly patterns and norm patterns which capture both partial-value and full-value variable relations as well as interactive, concurrent effects of multiple variables.

Cite this Paper

BibTeX
@InProceedings{pmlr-v71-ye18a, title = {Analytical Techniques for Anomaly Detection Through Features, Signal-Noise Separation and Partial-Value Association}, author = {Ye, Nong}, booktitle = {Proceedings of the KDD 2017: Workshop on Anomaly Detection in Finance}, pages = {20--32}, year = {2018}, editor = {Anandakrishnan, Archana and Kumar, Senthil and Statnikov, Alexander and Faruquie, Tanveer and Xu, Di}, volume = {71}, series = {Proceedings of Machine Learning Research}, month = {14 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v71/ye18a/ye18a.pdf}, url = {https://proceedings.mlr.press/v71/ye18a.html}, abstract = {This paper presents three analytical techniques for anomaly detection which can play an important role for anomaly detection in finance: the feature extraction technique, the signal-noise separation technique, and the Partial-Value Association Discovery (PVAD) algorithm. The feature extraction technique emphasizes the importance of extracting various data features which may be better at separating anomalies from norms than using raw data. The signal-noise separation technique considers an anomaly as the signal to detect and the norm as the noise and employs both anomaly models and norm models to detect anomalies accurately. The PVAD algorithm enables learning from data to build anomaly patterns and norm patterns which capture both partial-value and full-value variable relations as well as interactive, concurrent effects of multiple variables.} }
Endnote
%0 Conference Paper %T Analytical Techniques for Anomaly Detection Through Features, Signal-Noise Separation and Partial-Value Association %A Nong Ye %B Proceedings of the KDD 2017: Workshop on Anomaly Detection in Finance %C Proceedings of Machine Learning Research %D 2018 %E Archana Anandakrishnan %E Senthil Kumar %E Alexander Statnikov %E Tanveer Faruquie %E Di Xu %F pmlr-v71-ye18a %I PMLR %P 20--32 %U https://proceedings.mlr.press/v71/ye18a.html %V 71 %X This paper presents three analytical techniques for anomaly detection which can play an important role for anomaly detection in finance: the feature extraction technique, the signal-noise separation technique, and the Partial-Value Association Discovery (PVAD) algorithm. The feature extraction technique emphasizes the importance of extracting various data features which may be better at separating anomalies from norms than using raw data. The signal-noise separation technique considers an anomaly as the signal to detect and the norm as the noise and employs both anomaly models and norm models to detect anomalies accurately. The PVAD algorithm enables learning from data to build anomaly patterns and norm patterns which capture both partial-value and full-value variable relations as well as interactive, concurrent effects of multiple variables.
APA
Ye, N.. (2018). Analytical Techniques for Anomaly Detection Through Features, Signal-Noise Separation and Partial-Value Association. Proceedings of the KDD 2017: Workshop on Anomaly Detection in Finance, in Proceedings of Machine Learning Research 71:20-32 Available from https://proceedings.mlr.press/v71/ye18a.html.

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