Paper Info
Title
R1STM: One-class Support Tensor Machine with Randomised Kernel.
Abstract
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 detectionmethod, 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.
Year
Venue
Field
2016
SDM
Kernel (linear algebra),Anomaly detection,Nonlinear system,Pattern recognition,Tensor,Computer science,Generalization,Support vector machine,Curse of dimensionality,Artificial intelligence,Deep learning,Machine learning
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
7
Name
Order
Citations
PageRank
Sarah M. Erfani123623.58
Mahsa Baktashmotlagh220913.28
Sutharshan Rajasegarar365440.38
Xuan Vinh Nguyen474942.94
Christopher Leckie52422155.20
James Bailey62172164.56
kotagiri ramamohanarao74716993.87