ONLINE GRAPH LEARNING FROM SEQUENTIAL DATA
ONLINE GRAPH LEARNING FROM SEQUENTIAL DATA
复制标题
从序列数据中进行在线图学习
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
A. H. Sayed
中科院分区：
文献类型：
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作者：
Stefan Vlaski;Hermina Petric Maretic;Roula Nassif;P. Frossard;A. H. Sayed
图提供了一个强大的框架来表示高维但结构化的数据，并对数据子集之间的关系进行推断。在这项工作中，我们考虑了根据热扩散过程动态演化并受到持续扰动的图信号。我们开发了一个在线算法，该算法能够从信号演变的观察中学习潜在的图形结构。该算法本质上是自适应的，特别是能够响应图结构和扰动统计的变化。
Graphs provide a powerful framework to represent high-dimensional but structured data, and to make inferences about relationships between subsets of the data. In this work we consider graph signals that evolve dynamically according to a heat diffusion process and are subject to persistent perturbations. We develop an online algorithm that is able to learn the underlying graph structure from observations of the signal evolution. The algorithm is adaptive in nature and in particular able to respond to changes in the graph structure and the perturbation statistics.