High-dimensional model recovery from random sketched data by exploring intrinsic sparsity

Machine Learning, pp. 899-938, 2020.

Cited by: 0|Bibtex|Views91|DOI:https://doi.org/10.1007/s10994-019-05865-4
EI
Other Links: academic.microsoft.com|dblp.uni-trier.de|link.springer.com

Abstract:

Learning from large-scale and high-dimensional data still remains a computationally challenging problem, though it has received increasing interest recently. To address this issue, randomized reduction methods have been developed by either reducing the dimensionality or reducing the number of training instances to obtain a small sketch of...More

Code:

Data:

Your rating :
0

 

Tags
Comments