Database-friendly random projections

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Abstract

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.

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APA

Achlioptas, D. (2001). Database-friendly random projections. In Proceedings of the ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems (pp. 274–281). Association for Computing Machinery (ACM). https://doi.org/10.1145/375551.375608

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