2019 27th European Signal Processing Conference (EUSIPCO) 2019
DOI: 10.23919/eusipco.2019.8902344
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Topology Inference and Signal Representation Using Dictionary Learning

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“…Some other research works with similar ideas are presented for different stationary and non-stationary processes in [26][27][28][29][30]. Dictionary learning [31,32] and transform learning [33] have also been used for inferring the graph topology. In [31][32][33], a specific relation between the Laplacian matrix and the dictionary atoms has been sought and hence these algorithms are applicable when we have some knowledge about signal representation in Fig.…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…Some other research works with similar ideas are presented for different stationary and non-stationary processes in [26][27][28][29][30]. Dictionary learning [31,32] and transform learning [33] have also been used for inferring the graph topology. In [31][32][33], a specific relation between the Laplacian matrix and the dictionary atoms has been sought and hence these algorithms are applicable when we have some knowledge about signal representation in Fig.…”
Section: Introductionmentioning
confidence: 99%
“…Dictionary learning [31,32] and transform learning [33] have also been used for inferring the graph topology. In [31][32][33], a specific relation between the Laplacian matrix and the dictionary atoms has been sought and hence these algorithms are applicable when we have some knowledge about signal representation in Fig. 1 Illustration of learning a network graph structure from IoT data 1 The GSO is a matrix which captures the graph's local topology and the graph Fourier transform is defined using its eigenvectors.…”
Section: Introductionmentioning
confidence: 99%