Sciweavers

PODS
2001
ACM

Database-friendly random projections

14 years 3 months ago
Database-friendly random projections
A classic result of Johnson and Lindenstrauss asserts that any set of n points in d-dimensional Euclidean space can be embedded into k-dimensional Euclidean space -- where k is logarithmic in n and independent of d -- so that all pairwise distances are maintained within an arbitrarily small factor. All known constructions of such embeddings involve projecting the n points onto a random k-dimensional hyperplane. We give a novel construction of the embedding, suitable for database applications, which amounts to computing a simple aggregate over k random attribute partitions.
Dimitris Achlioptas
Added 08 Dec 2009
Updated 08 Dec 2009
Type Conference
Year 2001
Where PODS
Authors Dimitris Achlioptas
Comments (0)