2 papers
cs.DS2022
Johnson-Lindenstrauss embeddings for noisy vectors -- taking advantage of the noise
Zhen Shao
This paper investigates theoretical properties of subsampling and hashing as tools for approximate Euclidean norm-preserving embeddings for vectors with (unknown) additive Gaussian…
math.OC2022
On random embeddings and their application to optimisation
Zhen Shao
Random embeddings project high-dimensional spaces to low-dimensional ones; they are careful constructions which allow the approximate preservation of key properties, such as the pa…