R1STM: One-class support tensor machine with randomised kernel
Erfani, Sarah, Baktashmotlagh, Mahsa, Rajasegarar, Sutharshan, Nguyen, Vinh, Leckie, Chris, Bailey, James, & Kotagiri, Ramamohanarao (2016) R1STM: One-class support tensor machine with randomised kernel. In SIAM International Conference on Data Mining (SDM16), 5-7 May 2016, Miami, FL.
Identifying unusual or anomalous patterns in an underlying dataset is an important but challenging task in many applications. The focus of the unsupervised anomaly detection literature has mostly been on vectorised data. However, many applications are more naturally described using higher-order tensor representations. Approaches that vectorise tensorial data can destroy the structural information encoded in the high-dimensional space, and lead to the problem of the curse of dimensionality. In this paper we present the first unsupervised tensorial anomaly detection method, along with a randomised version of our method. Our anomaly detection method, the One-class Support Tensor Machine (1STM), is a generalisation of conventional one-class Support Vector Machines to higher-order spaces. 1STM preserves the multiway structure of tensor data, while achieving significant improvement in accuracy and efficiency over conventional vectorised methods. We then leverage the theory of nonlinear random projections to propose the Randomised 1STM (R1STM). Our empirical analysis on several real and synthetic datasets shows that our R1STM algorithm delivers comparable or better accuracy to a state-of-the-art deep learning method and traditional kernelised approaches for anomaly detection, while being approximately 100 times faster in training and testing.
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|Item Type:||Conference Paper|
|Divisions:||Current > QUT Faculties and Divisions > Science & Engineering Faculty|
|Copyright Owner:||Copyright 2016 [Please consult the author]|
|Deposited On:||29 Mar 2016 02:03|
|Last Modified:||07 May 2016 14:00|
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