Links: A High-Dimensional Online Clustering Method

2018/01/30 by P. Mansfield, Quan Wang, Mansfield, Philip Andrew +7 · 3 citations
Computer Science · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Speech and Audio Processing #Video Analysis and Summarization #Video Surveillance and Tracking Methods

paper · pdf · doi:10.48550/arxiv.1801.10123

openalex publication_date 2018/01/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We present a novel algorithm, called Links, designed to perform online clustering on unit vectors in a high-dimensional Euclidean space. The algorithm is appropriate when it is necessary to cluster data efficiently as it streams in, and is to be contrasted with traditional batch clustering algorithms that have access to all data at once. For example, Links has been successfully applied to embedding vectors generated from face images or voice recordings for the purpose of recognizing people, thereby providing real-time identification during video or audio capture.

Citations

• Generalized End-to-End Loss for Speaker Verification

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