TY - GEN
T1 - Least-squares regression with unitary constraints for network behaviour classification
AU - Robles-Kelly, Antonio
N1 - Publisher Copyright:
© Springer International Publishing AG 2016.
PY - 2016
Y1 - 2016
N2 - 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 leastsquares 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.
AB - 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 leastsquares 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.
UR - http://www.scopus.com/inward/record.url?scp=84996921137&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-49055-7_3
DO - 10.1007/978-3-319-49055-7_3
M3 - Conference contribution
SN - 9783319490540
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 26
EP - 36
BT - Structural, Syntactic, and Statistical Pattern Recognition - Joint IAPR International Workshop S+SSPR 2016, Proceedings
A2 - Biggio, Battista
A2 - Wilson, Richard
A2 - Loog, Marco
A2 - Escolano, Francisco
A2 - Robles-Kelly, Antonio
PB - Springer Verlag
T2 - Joint IAPR International Workshops on Structural and Syntactic Pattern Recognition, SSPR 2016
Y2 - 29 November 2016 through 2 December 2016
ER -