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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Vydáno v:	IEEE transactions on signal processing Ročník 64; číslo 23; s. 6160 - 6173
Hlavní autoři:	Xiaowen Dong, Thanou, Dorina, Frossard, Pascal, Vandergheynst, Pierre
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	IEEE 01.12.2016
Témata:	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
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