Kernelization of matrix updates, when and how?

We define what it means for a learning algorithm to be kernelizable in the case when the instances are vectors, asymmetric matrices and symmetric matrices, respectively. We can characterize kernelizability in terms of an invariance of the algorithm to certain orthogonal transformations. If we assume...

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Bibliographic Details
Published in:	Theoretical computer science Vol. 558; pp. 159 - 178
Main Authors:	Warmuth, Manfred K., Kotłowski, Wojciech, Zhou, Shuisheng
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 13.11.2014
Subjects:	Algorithms Asymmetry Exponentiated Gradient algorithm Gradient Descent algorithm Invariance Kernelization Learning Linear prediction Mathematical analysis Multiplicative updates Rotational invariance Transformations Vectors (mathematics) Multiplicative updates Exponentiated Gradient algorithm Gradient Descent algorithm Rotational invariance Kernelization
ISSN:	0304-3975, 1879-2294
Online Access:	Get full text
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