SCAR-Reference-Cited by-同舟云学术

SCAR

Published:2022-07 Issue:11 Volume:15 Page:3031-3044
ISSN:2150-8097
Container-title:Proceedings of the VLDB Endowment
language:en
Short-container-title:Proc. VLDB Endow.

Author:

Hohma Ellen¹,Frey Christian M. M.²,Beer Anna³,Seidl Thomas⁴

Affiliation:

1. Technical University of Munich, Munich, Germany

2. Christian-Albrecht University of Kiel, Kiel, Germany

3. Aarhus University, Aarhus, Denmark

4. LMU Munich, Munich, Germany

Abstract

Spectral clustering is one of the most advantageous clustering approaches. However, standard Spectral Clustering is sensitive to noisy input data and has a high runtime complexity. Tackling one of these problems often exacerbates the other. As real-world datasets are often large and compromised by noise, we need to improve both robustness and runtime at once. Thus, we propose Spectral Clustering - Accelerated and Robust (SCAR), an accelerated, robustified spectral clustering method. In an iterative approach, we achieve robustness by separating the data into two latent components: cleansed and noisy data. We accelerate the eigendecomposition - the most time-consuming step - based on the Nyström method. We compare SCAR to related recent state-of-the-art algorithms in extensive experiments. SCAR surpasses its competitors in terms of speed and clustering quality on highly noisy data.

Publisher

Association for Computing Machinery (ACM)

Subject

General Earth and Planetary Sciences,Water Science and Technology,Geography, Planning and Development

Link

https://dl.acm.org/doi/pdf/10.14778/3551793.3551850

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