%0 Generic %1 stoll2019literature %A Stoll, Martin %D 2019 %K 2019 linear-algebra machine-learning matrix survey %T A literature survey of matrix methods for data science %U http://arxiv.org/abs/1912.07896 %X Efficient numerical linear algebra is a core ingredient in many applications across almost all scientific and industrial disciplines. With this survey we want to illustrate that numerical linear algebra has played and is playing a crucial role in enabling and improving data science computations with many new developments being fueled by the availability of data and computing resources.

@misc{stoll2019literature, abstract = {Efficient numerical linear algebra is a core ingredient in many applications across almost all scientific and industrial disciplines. With this survey we want to illustrate that numerical linear algebra has played and is playing a crucial role in enabling and improving data science computations with many new developments being fueled by the availability of data and computing resources.}, added-at = {2020-01-05T07:48:01.000+0100}, author = {Stoll, Martin}, biburl = {https://www.bibsonomy.org/bibtex/246b2f141a1227e910c1f461e43190431/analyst}, description = {[1912.07896] A literature survey of matrix methods for data science}, interhash = {afe11661ec0931dff04e926e18884004}, intrahash = {46b2f141a1227e910c1f461e43190431}, keywords = {2019 linear-algebra machine-learning matrix survey}, note = {cite arxiv:1912.07896}, timestamp = {2020-01-05T07:48:01.000+0100}, title = {A literature survey of matrix methods for data science}, url = {http://arxiv.org/abs/1912.07896}, year = 2019 }