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Least-Squares Regression with Unitary Constraints for Network Behaviour Classification

  • Antonio Robles-KellyEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10029)

Abstract

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.

Keywords

Support Vector Machine Radial Basis Function Linear Discriminant Analysis Network Behaviour Attack Detection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.DATA61 - CSIROCanberra ACTAustralia
  2. 2.College of Engineering and Computer ScienceAustralian National UniversityCanberraAustralia

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