Learning Laplacian Matrix in Smooth Graph Signal Representations

The construction of a meaningful graph plays a crucial role in the success of many graph-based representations and algorithms for handling structured data, especially in the emerging field of graph signal processing. However, a meaningful graph is not always readily available from the data, nor easy...

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Veröffentlicht in:	IEEE transactions on signal processing Jg. 64; H. 23; S. 6160 - 6173
Hauptverfasser:	Xiaowen Dong, Thanou, Dorina, Frossard, Pascal, Vandergheynst, Pierre
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	IEEE 01.12.2016
Schlagworte:	Analytical models factor analysis Gaussian prior graph signal processing Kernel Laplace equations Laplacian matrix learning representation theory Signal processing Signal processing algorithms Signal representation Topology
ISSN:	1053-587X, 1941-0476
Online-Zugang:	Volltext
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