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...

• Type: Conference
• Year: 2008
• Where: CIKM
• Authors: Yu-En Lu, Pietro Liò, Steven Hand
• Keywords: CIKM 2008, Information Management, Random Projection, Rp Algorithms, Similarity Metrics