Multivariate signal restoration via Multifold Graph Learning

Abstract

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 retaining common connectivity patterns. We refer to the graphs associated with individual components and windows as multifold graphs. We model multifold graphs as nonnegative combinations of shared prototype graphs. We formulate signal restoration and multifold graph learning as a joint optimization problem and solve it by alternating optimization. We unroll the resulting algorithm into a neural network with trainable regularization weights, step sizes, and filter coefficients. The network is trained in a self-supervised manner. Synthetic experiments show improved graph estimation accuracy over the compared methods. On real-world weather data, the proposed network achieves competitive completion performance with substantially fewer trainable parameters than a self-attention-based model.