Database-friendly random projections

Dimitris Achlioptas
2001 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems - PODS '01  
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
more » ... applications, which amounts to computing a simple aggregate over k random attribute partitions.
doi:10.1145/375551.375608 dblp:conf/pods/Achlioptas01 fatcat:yx5rbynspbdcjcfuotchmc5qam