TITLE: Sketching Datasets for Large-Scale Learning (long version) 

ABSTRACT: This article considers "compressive learning," an approach to large-scale
machine learning where datasets are massively compressed before learning (e.g.,
clustering, classification, or regression) is performed. In particular, a
"sketch" is first constructed by computing carefully chosen nonlinear random
features (e.g., random Fourier features) and averaging them over the whole
dataset. Parameters are then learned from the sketch, without access to the
original dataset. This article surveys the current state-of-the-art in
compressive learning, including the main concepts and algorithms, their
connections with established signal-processing methods, existing theoretical
guarantees -- on both information preservation and privacy preservation, and
important open problems.
This article considers "compressive learning," an approach to large-scale
machine learning where datasets are massively compressed before learning (e.g.,
clustering, classification, or regression) is performed. In particular, a
"sketch" is first constructed by computing carefully chosen nonlinear random
features (e.g., random Fourier features) and averaging them over the whole
dataset. Parameters are then learned from the sketch, without access to the
original dataset. This article surveys the current state-of-the-art in
compressive learning, including the main concepts and algorithms, their
connections with established signal-processing methods, existing theoretical
guarantees -- on both information preservation and privacy preservation, and
important open problems.
Sketching Datasets for Large-Scale Learning (long version)
http://arxiv.org/abs/2008.01839v3