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Multivariate signal restoration via Multifold Graph Learning
We propose a method for restoring noisy and incomplete multivariate signals by learning multifold graphs. Spatial relationships among channels can be utilized to recover missing values and suppress noise. However, these relationships can vary across frequency components and observation windows while
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