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Dimensionality Reduction for Similarity Searching in Dynamic Databases
1999
Computer Vision and Image Understanding
Databases are increasingly being used to store multi-media objects such as maps, images, audio and video. Storage and retrieval of these objects is accomplished using multi-dimensional index structures such as R ¢ -trees and SS-trees. As dimensionality increases, query performance in these index structures degrades. This phenomenon, generally referred to as the dimensionality curse, can be circumvented by reducing the dimensionality of the data. Such a reduction is however accompanied by a loss
doi:10.1006/cviu.1999.0762
fatcat:orevfr2wfzhbvoppqnbqkiqnna