Inference of spatiotemporal processes over graphs via kernel kriged Kalman filtering
Inference of spatiotemporal processes over graphs via kernel kriged Kalman filtering
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DOI：
10.23919/eusipco.2017.8081495
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发表时间：
2017-08
期刊：
影响因子：
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通讯作者：
V. Ioannidis;Daniel Romero;G. Giannakis
中科院分区：
文献类型：
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作者：
V. Ioannidis;Daniel Romero;G. Giannakis
时空信号在图上演化的推断在许多网络科学相关的应用中自然出现。一个经常遇到的挑战涉及到重建这样的动态过程给定其值在一个子集的顶点和时刻。本论文开发了一种基于图感知核的克里格卡尔曼滤波方法，该方法利用时空动态来进行有效的在线重建，同时还应对动态演变的网络拓扑结构。当空间二阶统计量未知时，拉普拉斯核被用来在图上执行克里金，这是经常发生的情况。用合成数据和真实的数据进行的数值试验表明，该方法具有上级重建性能。
Inference of space-time signals evolving over graphs emerges naturally in a number of network science related applications. A frequently encountered challenge pertains to reconstructing such dynamic processes given their values over a subset of vertices and time instants. The present paper develops a graph-aware kernel-based kriged Kalman filtering approach that leverages the spatio-temporal dynamics to allow for efficient online reconstruction, while also coping with dynamically evolving network topologies. Laplacian kernels are employed to perform kriging over the graph when spatial second-order statistics are unknown, as is often the case. Numerical tests with synthetic and real data illustrate the superior reconstruction performance of the proposed approach.