Why Are Big Data Matrices Approximately Low Rank? | Zendy

Madeleine Udell | Zendy; Alex Townsend | Zendy

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Why Are Big Data Matrices Approximately Low Rank?

Author(s) -

Madeleine Udell,

Alex Townsend

Publication year - 2019

Publication title -

siam journal on mathematics of data science

Language(s) - English

Resource type - Journals

ISSN - 2577-0187

DOI - 10.1137/18m1183480

Subject(s) - rank (graph theory) , latent variable , matrix (chemical analysis) , data matrix , bounded function , combinatorics , variable (mathematics) , low rank approximation , simple (philosophy) , function (biology) , mathematics , piecewise , computer science , statistics , pure mathematics , mathematical analysis , chemistry , clade , biochemistry , philosophy , epistemology , chromatography , evolutionary biology , tensor (intrinsic definition) , biology , gene , phylogenetic tree

Matrices of (approximate) low rank are pervasive in data science, appearing in movie preferences, text documents, survey data, medical records, and genomics. While there is a vast literature on how...

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