Approximate nearest neighbors: towards removing the curse of dimensionality
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Database-friendly random projections
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
A note on a method for generating points uniformly on n-dimensional spheres
Communications of the ACM
LSH forest: self-tuning indexes for similarity search
WWW '05 Proceedings of the 14th international conference on World Wide Web
Very sparse random projections
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Improving random projections using marginal information
COLT'06 Proceedings of the 19th annual conference on Learning Theory
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Random projection (RP) is a common technique for dimensionality reduction under L 2 norm for which many significant space embedding results have been demonstrated. In particular, random projection techniques can yield sharp results for R d under the L 2 norm in time linear to the product of the number of data points and dimensionalities in question. Inspired by the use of symmetric probability distributions in previous work, we propose a RP algorithm based on the hyper-spherical symmetry and give its probabilistic analyses based on Beta and Gaussian distribution.