Curvelet Based Multiresolution Analysis of Graph Neural Networks
This paper explores the application of curvelet transform in the analysis of such complex networks. Especially, a technique of Fast Discrete Curvelet Transform de-noising with the Independent Component Analysis (ICA) for the separation of noisy signals is discussed. Two different approaches viz. separating noisy mixed signals using fast ICA algorithm and then applying Curvelet thresholding to de-noise the resulting signal, and the other one that uses Curvelet thresholding to de-noise the mixed signals and then the fast ICA algorithm to separate the de-noised signals are presented for the purpose. The Signal-to-Noise Ratio and Root Mean Square Error are used as metrics to evaluate the quality of the separated signals.
Index Terms—Curvelet transform, graph neural networks, curvelet thresholding, denoising.
Bharat Bhosale is with S. H. Kelkar College of Arts, Commerce and Science, University of Mumbai, Devgad 416613 (M.S.), India (email: firstname.lastname@example.org).
Cite: Bharat Bhosale, "Curvelet Based Multiresolution Analysis of Graph Neural Networks," International Journal of Applied Physics and Mathematics vol. 4, no. 5, pp. 313-323, 2014.
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