Leveraging Reinforcement Learning for evaluating Robustness of KNN Search Algorithms

TL;DR

This paper introduces a reinforcement learning framework to evaluate the robustness of KNN search algorithms, addressing challenges in high-dimensional spaces and approximate methods.

Contribution

It proposes a novel RL-based framework to assess KNN robustness and explores relationships between true and false positives in high-dimensional searches.

Findings

RL framework effectively evaluates robustness against adversarial points

Relationships between true and false positives are characterized

Survey of novel KNN approaches considering computation and accuracy

Abstract

The problem of finding K-nearest neighbors in the given dataset for a given query point has been worked upon since several years. In very high dimensional spaces the K-nearest neighbor search (KNNS) suffers in terms of complexity in computation of high dimensional distances. With the issue of curse of dimensionality, it gets quite tedious to reliably bank on the results of variety approximate nearest neighbor search approaches. In this paper, we survey some novel K-Nearest Neighbor Search approaches that tackles the problem of Search from the perspectives of computations, the accuracy of approximated results and leveraging parallelism to speed-up computations. We attempt to derive a relationship between the true positive and false points for a given KNNS approach. Finally, in order to evaluate the robustness of a KNNS approach against adversarial points, we propose a generic Reinforcement Learning based framework for the same.