Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
395792 | Information Sciences | 2009 | 15 Pages |
Abstract
We propose SubSpace Projection (SSP) as a unified framework for these partition-based techniques. SSP projects data onto subspaces and computes a fixed number of salient features with respect to a reference vector. A study of the relationships between query selectivity and the corresponding space partitioning schemes uncovers indicators that can be used to predict the performance of the partitioning configuration. Accordingly, we design a greedy algorithm to efficiently determine a good partitioning of the data dimensions. The results of our extensive experiments indicate that the proposed method consistently outperforms state-of-the-art techniques.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Hao Cheng, Khanh Vu, Kien A. Hua,