fc8001f834f6a5f0561080d134d53d29-Reviews.html

Summary: The paper presents a method that learns a pruning algorithm for a VP-tree, in non-metric spaces. The idea is to estimate the decision function of the approximate nearest neighbor search in the VP-tree by sampling, and approximating it with a piecewise linear function. The learning to prune method is validated for the search efficiency against relevant baselines for prunning, and outperforms them substantially when the intrinsic dimensionality of the data is small. Clarity: The paper is mostly clearly written but sometimes does not really go into explaining the implementation details and the choice of some parameters (for example, why choose K 100, m 7, rho 8 and the bucket size 10 5? Line 185,227,315) Originality: Learning to approximate the approximate nearest neighbor classification on a VP-tree, to the extent of my knowledge, is the first work that'learns to prune' Significance: Nearest neighbor method is a very fundamental topic in search or classification; thus this learning-to-prune method which approximates the nearest neighbor search with a non-linear function would be of some interest to a wide audience. However, the datasets chosen for validation for the experiments seem rather simple and have low-dimensionality, which are far from realistic.