Adaptive Learning in Complex Reproducing Kernel Hilbert Spaces Employing Wirtinger's Subgradients

This paper presents a wide framework for non-linear online supervised learning tasks in the context of complex valued signal processing. The (complex) input data are mapped into a complex reproducing kernel Hilbert space (RKHS), where the learning phase is taking place. Both pure complex kernels and...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems Jg. 23; H. 3; S. 425 - 438
Hauptverfasser:	Bouboulis, P., Slavakis, K., Theodoridis, S.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York, NY IEEE 01.03.2012 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Adaptive kernel learning Applied sciences Artificial intelligence Calculus Channels complex kernels Computer science; control theory; systems Context Equalization Exact sciences and technology Hilbert space Kernel Kernels Learning Learning and adaptive systems Loss measurement Machine learning Mathematical analysis Nonlinearity On-line systems projection Signal processing Studies subgradient Tasks widely linear estimation Wirtinger's calculus complex kernels subgradient Non linear channel Complex signal Equalization widely linear estimation Adaptive method Quadrature phase shift keying Kernel method Complex variable method Loss function Supervised learning Efficiency Linear filter Signal processing Sparse representation Signal sources Adaptive kernel learning Hilbert space Linear estimation projection Wirtinger's calculus Learning algorithm
ISSN:	2162-237X, 2162-2388
Online-Zugang:	Volltext
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