SONNET: Efficient Approximate Nearest Neighbor Using Multi-core

Abstract

Approximate Nearest Neighbor search over high dimensional data is an important problem with a wide range of practical applications. In this paper, we propose SONNET, a simple multi-core friendly approximate nearest neighbor algorithm that is based on rank aggregation. SONNET is particularly suitable for very high dimensional data, its performance gets better as the dimension increases, whereas the majority of the existing algorithms show a reverse trend. Furthermore, most of the existing algorithms are hard to parallelize either due to the sequential nature of the algorithm or due to the inherent complexity of the algorithm. On the other hand, SONNET has inherent parallelism embedded in the core concept of the algorithm, which earns it almost a linear speed-up as the number of cores increases. Finally, SONNET is very easy to implement and it has an approximation parameter which is intuitively simple.