Kernelized Locality-Sensitive Hashing for Semi-Supervised Agglomerative Clustering

TL;DR

This paper introduces a fast semi-supervised agglomerative clustering method that uses kernelized LSH to approximate distances, significantly reducing computation time while leveraging labeled data for improved accuracy.

Contribution

The paper presents a novel kernelized LSH-based approach for scalable semi-supervised agglomerative clustering that combines efficient hashing with metric learning.

Findings

Reduces clustering computation time significantly

Achieves competitive precision and recall with less computation

Effectively incorporates labeled data for improved clustering

Abstract

Large scale agglomerative clustering is hindered by computational burdens. We propose a novel scheme where exact inter-instance distance calculation is replaced by the Hamming distance between Kernelized Locality-Sensitive Hashing (KLSH) hashed values. This results in a method that drastically decreases computation time. Additionally, we take advantage of certain labeled data points via distance metric learning to achieve a competitive precision and recall comparing to K-Means but in much less computation time.