A progressive k-d tree for approximate k-nearest neighbors

Abstract

We present a progressive algorithm for approximate k-nearest neighbor search. Although the use of k-nearest neighbor libraries (KNN) is common in many data analysis methods, most KNN algorithms can only be run when the whole dataset has been indexed, i.e., they are not online. Even the few online implementations are not progressive in the sense that the time to index incoming data is not bounded and can exceed the latency required by progressive systems. This latency significantly restricts the interactivity of visualization systems especially when dealing with large-scale data. We improve traditional k-d trees for progressive approximate k-nearest neighbor search, enabling fast KNN queries while continuously indexing new batches of data when necessary. Following the progressive computation paradigm, our progressive k-d tree is bounded in time, allowing analysts to access ongoing results within an interactive latency. We also present performance benchmarks to compare online and progressive k-d trees.