论文标题
基于双重动态网络拓扑的在线学习
Dual-based Online Learning of Dynamic Network Topologies
论文作者
论文摘要
我们研究了以流媒体方式获得的流畅淋巴结观察的在线网络拓扑识别。与非自适应批处理解决方案不同，我们的独特目标是通过在线处理信号快照，以负担得起的内存和计算成本来跟踪（可能）动态的邻接矩阵。为此，我们利用并截断了基于双重近端梯度（DPG）迭代来解决复合平滑度调节，时变逆问题。使用合成和真实的皮质学数据的数值测试显示了在跟踪缓慢变化的网络连接时，新型轻型迭代的有效性。我们还表明，在线DPG算法的收敛速度要比基于原始的复杂性基线更快。与可再现的研究实践保持一致，我们共享制定的代码，以生成本文中包含的所有数字。
We investigate online network topology identification from smooth nodal observations acquired in a streaming fashion. Different from non-adaptive batch solutions, our distinctive goal is to track the (possibly) dynamic adjacency matrix with affordable memory and computational costs by processing signal snapshots online. To this end, we leverage and truncate dual-based proximal gradient (DPG) iterations to solve a composite smoothness-regularized, time-varying inverse problem. Numerical tests with synthetic and real electrocorticography data showcase the effectiveness of the novel lightweight iterations when it comes to tracking slowly-varying network connectivity. We also show that the online DPG algorithm converges faster than a primal-based baseline of comparable complexity. Aligned with reproducible research practices, we share the code developed to produce all figures included in this paper.
