%0 Generic %1 vershynin2014estimation %A Vershynin, Roman %D 2014 %K 2014 arxiv geometry machine-learning optimization statistics %T Estimation in high dimensions: a geometric perspective %U http://arxiv.org/abs/1405.5103 %X This tutorial provides an exposition of a flexible geometric framework for high dimensional estimation problems with constraints. The tutorial develops geometric intuition about high dimensional sets, justifies it with some results of asymptotic convex geometry, and demonstrates connections between geometric results and estimation problems. The theory is illustrated with applications to sparse recovery, matrix completion, quantization, linear and logistic regression and generalized linear models.

@misc{vershynin2014estimation, abstract = {This tutorial provides an exposition of a flexible geometric framework for high dimensional estimation problems with constraints. The tutorial develops geometric intuition about high dimensional sets, justifies it with some results of asymptotic convex geometry, and demonstrates connections between geometric results and estimation problems. The theory is illustrated with applications to sparse recovery, matrix completion, quantization, linear and logistic regression and generalized linear models.}, added-at = {2018-07-13T23:03:53.000+0200}, author = {Vershynin, Roman}, biburl = {https://www.bibsonomy.org/bibtex/232a6110d1d7fd50caf59a30e622b07ce/analyst}, description = {[1405.5103] Estimation in high dimensions: a geometric perspective}, interhash = {0e5390732dde7c8fa1f474b0433ea2a5}, intrahash = {32a6110d1d7fd50caf59a30e622b07ce}, keywords = {2014 arxiv geometry machine-learning optimization statistics}, note = {cite arxiv:1405.5103Comment: 56 pages, 9 figures. Multiple minor changes}, timestamp = {2018-07-13T23:03:53.000+0200}, title = {Estimation in high dimensions: a geometric perspective}, url = {http://arxiv.org/abs/1405.5103}, year = 2014 }