R1SVM: A randomised nonlinear approach to large-scale anomaly detection

Erfani, Sarah, Baktashmotlagh, Mahsa, Rajasegarar, Sutharshan, Karunasekera, Shanika, & Leckie, Chris (2015) R1SVM: A randomised nonlinear approach to large-scale anomaly detection. In 29th AAAI Conference on Artificial Intelligence, January 25–30, 2015, Hyatt Regency in Austin, Texas.

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Abstract

The problem of unsupervised anomaly detection arises in a wide variety of practical applications. While one-class support vector machines have demonstrated their effectiveness as an anomaly detection technique, their ability to model large datasets is limited due to their memory and time complexity for training. To address this issue for supervised learning of kernel machines, there has been growing interest in random projection methods as an alternative to the computationally expensive problems of kernel matrix construction and sup-port vector optimisation. In this paper we leverage the theory of nonlinear random projections and propose the Randomised One-class SVM (R1SVM), which is an efficient and scalable anomaly detection technique that can be trained on large-scale datasets. Our empirical analysis on several real-life and synthetic datasets shows that our randomised 1SVM algorithm achieves comparable or better accuracy to deep auto encoder and traditional kernelised approaches for anomaly detection, while being approximately 100 times faster in training and testing.

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ID Code: 94229
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Divisions: Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2015 Association for the Advancement of Artificial
Intelligence.
Deposited On: 29 Mar 2016 03:05
Last Modified: 03 Apr 2016 04:53

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