Similarity search in high-dimensional vector spaces
著者
書誌事項
Similarity search in high-dimensional vector spaces
（Dissertationen zur Künstlichen Intelligenz, Bd. 74）
Infix, c2001
- :Aka
- :IOS Press
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内容説明・目次
内容説明
This dissertation addresses the problem of identifying the most similar objects in a database given a set of reference objects and a set of features. It investigates the so-called "Curse of Dimensionality", and presents an organization for NN-Search ("Nearest Neighbour Search") optimized for high-dimensional spaces - the so-called "Vector Approximation File" (VA-File). The text shows the superiority of the VA-File theoretically and through experiments. The VA-File is also discussed with reference to approximate search and parallel search in a cluster of workstations. This dissertaion also provides an indexing technique that allows for interactive-time similarity search even in huge databases.
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