Scalable Multicore k-NN Search via Subspace Clustering for Filtering

k Nearest Neighbors (k-NN) search is a widely used category of algorithms with applications in domains such as computer vision and machine learning. Despite the desire to process increasing amounts of high-dimensional data within these domains, k-NN algorithms scale poorly on multicore systems because they hit a memory wall. In this paper, we propose a novel data filtering strategy for k-NN search algorithms on multicore platforms. By excluding unlikely features during the k-NN search process, this strategy can reduce the amount of computation required as well as the memory footprint. It is complementary to the data selection strategies used in other state-of-the-art k-NN algorithms. A Subspace Clustering for Filtering (SCF) method is proposed to implement the data filtering strategy. Experimental results on four k-NN algorithms show that SCF can significantly improve their performance on three modern multicore platforms with only a small loss of search precision.