Approximate Nearest Neighbour Search on Dynamic Datasets: An Investigation

arXiv:2404.19284v1 Announce Type: new
Abstract: Approximate k-Nearest Neighbour (ANN) methods are often used for mining information and aiding machine learning on large scale high-dimensional datasets. ANN methods typically differ in the index structure used for accelerating searches, resulting in various recall/runtime trade-off points. For applications with static datasets, runtime constraints and dataset properties can be used to empirically select an ANN method with suitable operating characteristics. However, for applications with dynamic datasets, which are subject to frequent online changes (like …

abstract ann applications arxiv constraints cs.lg datasets dynamic index information investigation machine machine learning mining recall scale search trade trade-off type