التفاصيل البيبلوغرافية
| العنوان: |
Modelling and studying the effect of graph errors in graph signal processing. |
| المؤلفون: |
Miettinen, Jari1 (AUTHOR), Vorobyov, Sergiy A.1 (AUTHOR), Ollila, Esa1 (AUTHOR) esa.ollila@aalto.fi |
| المصدر: |
Signal Processing. Dec2021, Vol. 189, pN.PAG-N.PAG. 1p. |
| مصطلحات موضوعية: |
*SIGNAL processing, *REPRESENTATIONS of graphs, *INDEPENDENT component analysis, *SIGNAL filtering |
| مستخلص: |
• We formulate graph error models for the adjacency matrix, which help to quantify the deviation from the true matrix using a few parameters. • We study the structural effects of the proposed error models on the adjacency matrix. • The effects of different type of errors in adjacency matrix specification are illustrated in filtering of graph signal and ICA of graph signals. The first step for any graph signal processing (GSP) procedure is to learn the graph signal representation, i.e., to capture the dependence structure of the data into an adjacency matrix. Indeed, the adjacency matrix is typically not known a priori and has to be learned. However, it is learned with errors. A little attention has been paid to modelling such errors in the adjacency matrix, and studying their effects on GSP methods. However, modelling errors in the adjacency matrix will enable both to study the graph error effects in GSP and to develop robust GSP algorithms. In this paper, we therefore introduce practically justifiable graph error models. We also study, both analytically when possible and numerically, the graph error effect on the performance of GSP methods in different types of problems such as filtering of graph signals and independent component analysis of graph signals (graph decorrelation). [ABSTRACT FROM AUTHOR] |
| قاعدة البيانات: |
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