Metric dimension reduction: A snapshot of the Ribe program

09/07/2018

∙

The purpose of this article is to survey some of the context, achievements, challenges and mysteries of the field of metric dimension reduction, including new perspectives on major older results as well as recent advances.

READ FULL TEXT

Metric dimension reduction: A snapshot of the Ribe program

Dimension reduction in spatial regression with kernel SAVE method

Persistent homology for low-complexity models

On a metric generalization of the tt-degrees and effective dimension theory

Weakly-correlated synapses promote dimension reduction in deep neural networks

A Time-Triggered Dimension Reduction Algorithm for the Task Assignment Problem

Parameter-free Network Sparsification and Data Reduction by Minimal Algorithmic Information Loss

ε-isometric dimension reduction for incompressible subsets of ℓ_p

Metric dimension reduction: A snapshot of the Ribe program

Related Research

Dimension reduction in spatial regression with kernel SAVE method

Persistent homology for low-complexity models

On a metric generalization of the tt-degrees and effective dimension theory

Weakly-correlated synapses promote dimension reduction in deep neural networks

A Time-Triggered Dimension Reduction Algorithm for the Task Assignment Problem

Parameter-free Network Sparsification and Data Reduction by Minimal Algorithmic Information Loss

ε-isometric dimension reduction for incompressible subsets of ℓ_p