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Least-squares regression with unitary constraints for network behaviour classification

conference contribution
posted on 2016-01-01, 00:00 authored by Antonio Robles-KellyAntonio Robles-Kelly
In this paper, we propose a least-squares regression method [2] with unitary constraints with applications to classification and recognition. To do this, we employ a kernel to map the input instances to a feature space on a sphere. In a similar fashion, we view the labels associated with the training data as points which have been mapped onto a Stiefel manifold using random rotations. In this manner, the least-squares problem becomes that of finding the span and kernel parameter matrices that minimise the distance between the embedded labels and the instances on the Stiefel manifold under consideration. We show the effectiveness of our approach as compared to alternatives elsewhere in the literature for classification on synthetic data and network behaviour log data, where we present results on attack identification and network status prediction.

History

Event

International Association of Pattern Recognition. Conference (2016 : Mérida, Mexico)

Volume

10029

Series

International Association of Pattern Recognition Conference

Pagination

26 - 36

Publisher

Springer

Location

Mérida, Mexico

Place of publication

Cham, Switzerland

Start date

2016-11-29

End date

2016-12-02

ISBN-13

978-3-319-49055-7

Language

eng

Publication classification

E1.1 Full written paper - refereed

Copyright notice

2016, Springer International Publishing AG

Editor/Contributor(s)

A Robles-Kelly, M Loog, B Biggio, F Escolano, R Wilson

Title of proceedings

S+SSPR 2016 : Proceedings of the Joint IAPR International Workshops on Structural and Syntactic Pattern Recognition (SSPR 2016) and Statistical Techniques in Pattern Recognition (SPR 2016)