Extensions of Approximate Nearest Neighbor Search Based on a Multi-Valued Expression of Closeness to General Distributions
Language
Japanese
Type
Proceedings of Japanese Conferences
Authors
Masashi Tada, Tomoyuki Muto, Masakazu Iwamura, Koichi Kise
Title
Extensions of Approximate Nearest Neighbor Search Based on a Multi-Valued Expression of Closeness to General Distributions
Journal
Proc. First Forum on Data Engineering and Information Management
Presentation number
F3-2
Date
February 2010
Research fields
Nearest Neighbor Search
Key
Tada2010_FDEI (id=1139)
Abstract
Approximate nearest neighbor search is a technique which greatly
reduces processing time and required amount of memory
for nearest neighbor search.
We proposed two approximate nearest neighbor methods: MVH1 and MVH2.
We comfirmed the effectiveness of these methods for uniform distribution with L1 norm.
In this report, we get rid of the restrictions of these methods and propose efficient methods for Lp norm and general distributions.
For L2 norm, improved MVH1 achived about 75% of processing time of LSH.
Improved MVH2 achived about 50% of required amount of memory of LSH.
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