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...

Celý popis

Podrobná bibliografie

Vydáno v:	Digital Signal Processing with Kernel Methods s. 543 - 588
Hlavní autoři:	Rojo-Álvarez, José Luis, Martínez-Ramón, Manel, Muñoz-Mar&iacute, Jordi, Camps-Valls, Gustau
Médium:	Kapitola
Jazyk:	angličtina
Vydáno:	Chichester, UK Wiley 2018 John Wiley & Sons, Ltd
Vydání:	1
Edice:	Wiley - IEEE
Témata:	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
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!