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CIKM
2008
Springer

On low dimensional random projections and similarity search

13 years 5 months ago
On low dimensional random projections and similarity search
Random projection (RP) is a common technique for dimensionality reduction under L2 norm for which many significant space embedding results have been demonstrated. However, many similarity search applications often require very low dimension embeddings in order to reduce overhead and boost performance. Inspired by the use of symmetric probability distributions in previous work, we propose a novel RP algorithm, Beta Random Projection, and give its probabilistic analyses based on Beta and Gaussian approximations. We evaluate the algorithm in terms of standard similarity metrics with other RP algorithms as well as the singular value decomposition (SVD). Our experimental results show that BRP preserves both similarity metrics well and, under various dataset types including random point sets, text (TREC5) and images, provides sharper and consistent performance. Categories and Subject Descriptors G.3 [Mathematics of Computing]: PROBABILITY AND STATISTICS; F.2 [Theory of Computation]: ANALYSI...
Yu-En Lu, Pietro Liò, Steven Hand
Added 12 Oct 2010
Updated 12 Oct 2010
Type Conference
Year 2008
Where CIKM
Authors Yu-En Lu, Pietro Liò, Steven Hand
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