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值得一提的是，科学家们已经利用SVD改进了地面引力波干涉仪aLIGO的[[引力波形建模]]（gravitational wave modeling）。<ref>{{citation | last1=Setyawati | first1=Y. | last2=Ohme | first2=F. | last3=Khan | first3=S. | date=2019 | title="Enhancing gravitational waveform model through dynamic calibration" | journal=Physical Review D | volume=99 | issue=2 | pages=024010 | doi=10.1103/PhysRevD.99.024010 | arxiv=1810.07060 | bibcode=2019PhRvD..99b4010S | s2cid=118935941}}</ref>SVD有助于提高波形生成的准确性和速度，支持引力波搜索和更新两种不同的波形模型。

值得一提的是，科学家们已经利用SVD改进了地面引力波干涉仪aLIGO的[[引力波形建模]]（gravitational wave modeling）。<ref>{{citation | last1=Setyawati | first1=Y. | last2=Ohme | first2=F. | last3=Khan | first3=S. | date=2019 | title="Enhancing gravitational waveform model through dynamic calibration" | journal=Physical Review D | volume=99 | issue=2 | pages=024010 | doi=10.1103/PhysRevD.99.024010 | arxiv=1810.07060 | bibcode=2019PhRvD..99b4010S | s2cid=118935941}}</ref>SVD有助于提高波形生成的准确性和速度，支持引力波搜索和更新两种不同的波形模型。

[[推荐系统]]（Recommender systems）中，SVD用于预测用户对项目的评分。<ref>{{citation | last1=Sarwar | first1=Badrul | last2=Karypis | first2=George | last3=Konstan | first3=Joseph A. | last4=Riedl | first4=John T. | date=2000 |url=https://files.grouplens.org/papers/webKDD00.pdf| title="Application of Dimensionality Reduction in Recommender System – A Case Study" | publisher=University of Minnesota}}</ref>为了在商品机器集群上高效计算SVD，研究人员开发了分布式算法。<ref>{{citation | last1=Bosagh Zadeh | first1=Reza | last2=Carlsson | first2=Gunnar | date=2013 | title="Dimension Independent Matrix Square Using MapReduce" | arxiv=1304.1467 | bibcode=2013arXiv1304.1467B}}</ref>

[[推荐系统]]（Recommender systems）中，SVD用于预测用户对项目的评分。<ref>{{citation | last1=Sarwar | first1=Badrul | last2=Karypis | first2=George | last3=Konstan | first3=Joseph A. | last4=Riedl | first4=John T. | date=2000 |url=https://files.grouplens.org/papers/webKDD00.pdf| title="Application of Dimensionality Reduction in Recommender System – A Case Study" | publisher=University of Minnesota}}</ref>为了在商品机器集群上高效计算SVD，研究人员开发了分布式算法。<ref>{{citation | last1=Bosagh Zadeh | first1=Reza | last2=Carlsson | first2=Gunnar | date=2013 |url=https://stanford.edu/~rezab/papers/dimsum.pdf| title="Dimension Independent Matrix Square Using MapReduce" | arxiv=1304.1467 | bibcode=2013arXiv1304.1467B}}</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}}</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}}</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}}</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}}</ref>