Multilabel Classification Framework for Approximate Nearest Neighbor Search

Explore a conference talk on a novel multilabel classification framework for approximate nearest neighbor (ANN) search. Delve into how space partitioning trees can be interpreted as multilabel classifiers, and learn about the potential of using supervised classifiers like random forests as index structures for ANN search. Discover the implications of this new approach for improving ANN search performance and opening up new research directions in this established algorithmic problem. Gain insights from Ville Hyvönen's presentation, based on joint work with Elias Jääsaari and Professor Teemu Roos, which was featured at the Neural Information Processing Systems (NeurIPS) 2022 conference.

Intro
Background
Rational binary classification
Research question
Machine learning problem
Multilevel classification
Results
Random Forest
Statistical Learning
Normalized Intuition
Future research directions