TIME-VARYING GRAPH LEARNING BASED ON SPARSENESS OF TEMPORAL VARIATION

被引：0

作者：

Yamada, Koki ^{[1
]}

Tanaka, Yuichi ^{[1
,2
]}

Ortega, Antonio ^{[3
]}

机构：

[1] Tokyo Univ Agr & Technol, Grad Sch BASE, Tokyo, Japan

[2] Japan Sci & Technol Agcy, PRESTO, Saitama, Japan

[3] Univ Southern Calif, Dept Elect & Comp Engn, Los Angeles, CA USA

来源：

2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) | 2019年

关键词：

Graph learning; time-varying graph; time-varying network; network inference; dynamic graph; NETWORKS;

D O I：

暂无

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

We propose a method for graph learning from spatiotemporal measurements. We aim at inferring time-varying graphs under the assumption that changes in graph topology and weights are sparse in time. The problem is formulated as a convex optimization problem to impose a constraint on the temporal relation of the time-varying graph. Experimental results with synthetic data show the effectiveness of our proposed method.

引用

页码：5411 / 5415

页数：5

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