SPARSE TOPOLOGY IDENTIFICATION FOR POINT PROCESS NETWORKS

被引：0

作者：

Pasha, Syed Ahmed ^{[1
]}

Solo, Victor ^{[2
]}

机构：

[1] Air Univ, Dept Elect Engn, Islamabad 44000, Pakistan

[2] Univ New South Wales, Sch Elect Engn & Telecom, Sydney, NSW 2052, Australia

来源：

2018 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) | 2018年

基金：

澳大利亚研究理事会;

关键词：

genomics; network topology; point process; system identification; sparsity; DYNAMIC NETWORKS;

D O I：

暂无

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

Increasingly in the 'big data' era, point process network data is appearing e.g. in social media but also genomics, high frequency finance and neurosicence. And the most basic problem is that of identifying network structure. As with all network system identification problems this is enabled by the use of sparsity. Here, for the first time, we develop a vector l(0) sparsity approach for identification of a network of interacting Hawkes processes. We apply the new method to genomic data on a transcriptional regulatory network in embryonic stem cells and the results are compared with previous work and experimental findings.

引用

页码：2196 / 2200

页数：5

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