论文标题
使用多编码卷积过滤器学习
Learning with Multigraph Convolutional Filters
论文作者
论文摘要
在本文中，我们介绍了卷积架构，以在支持多编码的信息时进行学习。利用代数信号处理（ASP），我们提出了一个关于多编码（MSP）的卷积信号处理模型。然后，我们引入多卷卷积神经网络（MGNN），作为堆叠和分层结构，其中根据MSP模型处理信息。我们还开发了一个可以在MGNN中滤过滤波器系数的计算的程序，并开发了一种低成本方法，以降低层之间传输的信息的维度。最后，我们将MGNN与其他学习架构的性能与多通道通信系统的最佳资源分配任务进行比较。
In this paper, we introduce a convolutional architecture to perform learning when information is supported on multigraphs. Exploiting algebraic signal processing (ASP), we propose a convolutional signal processing model on multigraphs (MSP). Then, we introduce multigraph convolutional neural networks (MGNNs) as stacked and layered structures where information is processed according to an MSP model. We also develop a procedure for tractable computation of filter coefficients in the MGNN and a low cost method to reduce the dimensionality of the information transferred between layers. We conclude by comparing the performance of MGNNs against other learning architectures on an optimal resource allocation task for multi-channel communication systems.
