Year: 2026
Type: preprint
Abstract: The Johnson-Lindenstrauss (JL) lemma and its extensions provide tools for solving the $L_2$ regression problem in high-dimensional settings with $n>d$, by transforming a subspace of $\mathbb{R}^{n}$ to a subspace of $\mathbb{R}^{m}$, (where $m
Source: arXiv (Cornell University)
Institution: Indian Statistical Institute
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: Indian Statistical Institute
OpenAlex ID: W7225146744
