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Redundant Bit Vectors for Searching High-Dimensional Regions
This video was recorded at Machine Learning Workshop, Sheffield 2004. Many multimedia applications reduce to the problem of searching a database of high-dimensional regions to see whether any overlap a query point. There is a large literature of indexing techniques based on trees, all of which break down given high enough dimension of stored regions. We have created a new data structure, called redundant bit vectors (RBVs), that can effectively index high-dimensional regions.Using RBVs, we can search a database of 240K 64-dimensional hyperspheres, each with a different radius, up to 56 times faster than an optimized learning scan. RBVs are general-purpose, and may be useful for machine learning applications.
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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
