低秩SVD在从时空数据中检测热点方面表现出色,已应用于疾病爆发检测<ref>{{citation | last1=Fanaee Tork | first1=Hadi | last2=Gama | first2=João | date=September 2014 | title="Eigenspace method for spatiotemporal hotspot detection" | journal=Expert Systems | volume=32 | issue=3 | pages=454–464 | doi=10.1111/exsy.12088 | arxiv=1406.3506 | bibcode=2014arXiv1406.3506F|s2cid=15476557}}</ref>。研究人员还将SVD和高阶SVD结合起来,用于疾病监测中从复杂数据流(具有空间和时间维度的多变量数据)进行实时事件检测<ref>{{citation | last1=Fanaee Tork | first1=Hadi | last2=Gama | first2=João | date=May 2015 | title="EigenEvent: An Algorithm for Event Detection from Complex Data Streams in Syndromic Surveillance" | journal=Intelligent Data Analysis | volume=19 | issue=3 | pages=597–616 | doi=10.3233/IDA-150734 | arxiv=1406.3496|s2cid=17966555}}</ref>。 | 低秩SVD在从时空数据中检测热点方面表现出色,已应用于疾病爆发检测<ref>{{citation | last1=Fanaee Tork | first1=Hadi | last2=Gama | first2=João | date=September 2014 | title="Eigenspace method for spatiotemporal hotspot detection" | journal=Expert Systems | volume=32 | issue=3 | pages=454–464 | doi=10.1111/exsy.12088 | arxiv=1406.3506 | bibcode=2014arXiv1406.3506F|s2cid=15476557}}</ref>。研究人员还将SVD和高阶SVD结合起来,用于疾病监测中从复杂数据流(具有空间和时间维度的多变量数据)进行实时事件检测<ref>{{citation | last1=Fanaee Tork | first1=Hadi | last2=Gama | first2=João | date=May 2015 | title="EigenEvent: An Algorithm for Event Detection from Complex Data Streams in Syndromic Surveillance" | journal=Intelligent Data Analysis | volume=19 | issue=3 | pages=597–616 | doi=10.3233/IDA-150734 | arxiv=1406.3496|s2cid=17966555}}</ref>。 |