CLC number: TP311
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2018-11-27
Cited: 0
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Qin Zhang, Guo-qiang Zhong, Jun-yu Dong. An anchor-based spectral clustering method[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(11): 1385-1396.
@article{title="An anchor-based spectral clustering method",
author="Qin Zhang, Guo-qiang Zhong, Jun-yu Dong",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="19",
number="11",
pages="1385-1396",
year="2018",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1700262"
}
%0 Journal Article
%T An anchor-based spectral clustering method
%A Qin Zhang
%A Guo-qiang Zhong
%A Jun-yu Dong
%J Frontiers of Information Technology & Electronic Engineering
%V 19
%N 11
%P 1385-1396
%@ 2095-9184
%D 2018
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1700262
TY - JOUR
T1 - An anchor-based spectral clustering method
A1 - Qin Zhang
A1 - Guo-qiang Zhong
A1 - Jun-yu Dong
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 19
IS - 11
SP - 1385
EP - 1396
%@ 2095-9184
Y1 - 2018
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1700262
Abstract: Spectral clustering is one of the most popular and important clustering methods in pattern recognition, machine learning, and data mining. However, its high computational complexity limits it in applications involving truly large-scale datasets. For a clustering problem with n samples, it needs to compute the eigenvectors of the graph Laplacian with O(n3) time complexity. To address this problem, we propose a novel method called anchor-based spectral clustering (ASC) by employing anchor points of data. Specifically, m (m≪n) anchor points are selected from the dataset, which can basically maintain the intrinsic (manifold) structure of the original data. Then a mapping matrix between the original data and the anchors is constructed. More importantly, it is proved that this data-anchor mapping matrix essentially preserves the clustering structure of the data. Based on this mapping matrix, it is easy to approximate the spectral embedding of the original data. The proposed method scales linearly relative to the size of the data but with low degradation of the clustering performance. The proposed method, ASC, is compared to the classical spectral clustering and two state-of-the-art accelerating methods, i.e., power iteration clustering and landmark-based spectral clustering, on 10 real-world applications under three evaluation metrics. Experimental results show that ASC is consistently faster than the classical spectral clustering with comparable clustering performance, and at least comparable with or better than the state-of-the-art methods on both effectiveness and efficiency.
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