Work

When lookout sees crackle: Anomaly detection via kernel density estimation

Year: 2026

Type: preprint

Abstract: We present an updated version of lookout -- an algorithm for detecting anomalies using kernel density estimates with bandwidth based on Rips death diameters -- with theoretical guarantees. The kernel density estimator for updated lookout is shown to be consistent, and the proposed multivariate scaling is robust and efficient. We show our updated algorithm performs better than the previous version on diverse examples.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2603.22636

Authors: Hyndman, Rob J, Kandanaarachchi, Sevvandi, Turner, Katharine

Institutions: Monash University, Commonwealth Scientific and Industrial Research Organisation, Australian National University

Topic: Anomaly Detection Techniques and Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7140296754

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