多元信号恢复：基于多折叠图学习
Multivariate signal restoration via Multifold Graph Learning
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中文总结 AI 辅助
提出一种基于多折叠图学习的多元信号恢复方法，通过联合优化与自监督展开网络，在合成和真实数据上实现高效补全。
中文摘要 AI 辅助
我们提出了一种通过学习多折叠图来恢复含噪且不完整的多元信号的方法。通道间的空间关系可用于恢复缺失值并抑制噪声。然而，这些关系可能随频率分量和观测窗口而变化，同时保留共同的连接模式。我们将与各个分量和窗口相关的图称为多折叠图。我们将多折叠图建模为共享原型图的非负组合。我们将信号恢复与多折叠图学习表述为一个联合优化问题，并通过交替优化求解。我们将所得算法展开为具有可训练正则化权重、步长和滤波器系数的神经网络。该网络以自监督方式进行训练。合成实验表明，与对比方法相比，所提方法在图估计精度上有所提升。在真实世界的气象数据上，所提网络以远少于基于自注意力模型的训练参数实现了具有竞争力的补全性能。
英文摘要
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.
发表机构
- Graduate School of Engineering, The University of Osaka（大阪大学大学院工学研究科）
机构由 AI 辅助整理，请以论文原文为准。
