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An adaptive and dynamic dimensionality reduction method for high-dimensional indexing
2005
The VLDB journal
The notorious "dimensionality curse" is a wellknown phenomenon for any multi-dimensional indexes attempting to scale up to high dimensions. One well-known approach to overcome degradation in performance with respect to increasing dimensions is to reduce the dimensionality of the original dataset before constructing the index. However, identifying the correlation among the dimensions and effectively reducing them are challenging tasks. In this paper, we present an adaptive Multi-level
doi:10.1007/s00778-005-0167-3
fatcat:gf5ihi7it5a3vehdsl2zqmz4u4