High-dimensional statistical learning: Roots, justifications, and potential machineries - Zollanvari, Amin (Cancer Informatics, 2016)
High-dimensional data generally refer to data in which the number of variables is larger than the sample size. Analyzing such datasets poses great challenges for classical statistical learning because the finite-sample performance of methods developed within classical statistical learning does not live up to classical asymptotic premises in which the sample size unboundedly grows for a fixed dimensionality of observations.
Keywords: Curse of dimensionality; Double asymptotics; G-analysis; High-dimensional analysis; Kolmogorov asymptotics; Random matrix theory; Ridge estimation; Shrinkage; Sparsity; Research Subject Categories::TECHNOLOGY::Chemical engineering