Kernel Feature Extraction in Signal Processing

Kernel‐based feature extraction and dimensionality reduction are becoming increasingly important in advanced signal processing. This is particularly relevant in applications dealing with very high‐dimensional data. Besides changing the data representation space via kernel featu...

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Bibliographic Details

Published in:	Digital Signal Processing with Kernel Methods pp. 543 - 588
Main Authors:	Rojo-Álvarez, José Luis, Martínez-Ramón, Manel, Muñoz-Mar&iacute, Jordi, Camps-Valls, Gustau
Format:	Book Chapter
Language:	English
Published:	Chichester, UK Wiley 2018 John Wiley & Sons, Ltd
Edition:	1
Series:	Wiley - IEEE
Subjects:	cross‐covariance operator Hilbert schmidt independence criterion kernel based feature extraction kernel dimensionality reduction methods signal processing Dimensionality reduction Correlation Signal processing Feature extraction Kernel Covariance matrices Principal component analysis
ISBN:	9781118611791, 1118611799
Online Access:	Get full text
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