SubSpace Projection: A unified framework for a class of partition-based dimension reduction techniques

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.