Large-Scale Spectral Clustering Based on Representative Points
Joint Authors
Nie, Feiping
Yang, Libo
Liu, Mingtang
Liu, Xuemei
Source
Mathematical Problems in Engineering
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-12-09
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
Spectral clustering (SC) has attracted more and more attention due to its effectiveness in machine learning.
However, most traditional spectral clustering methods still face challenges in the successful application of large-scale spectral clustering problems mainly due to their high computational complexity οn3, where n is the number of samples.
In order to achieve fast spectral clustering, we propose a novel approach, called representative point-based spectral clustering (RPSC), to efficiently deal with the large-scale spectral clustering problem.
The proposed method first generates two-layer representative points successively by BKHK (balanced k-means-based hierarchical k-means).
Then it constructs the hierarchical bipartite graph and performs spectral analysis on the graph.
Specifically, we construct the similarity matrix using the parameter-free neighbor assignment method, which avoids the need to tune the extra parameters.
Furthermore, we perform the coclustering on the final similarity matrix.
The coclustering mechanism takes advantage of the cooccurring cluster structure among the representative points and the original data to strengthen the clustering performance.
As a result, the computational complexity can be significantly reduced and the clustering accuracy can be improved.
Extensive experiments on several large-scale data sets show the effectiveness, efficiency, and stability of the proposed method.
American Psychological Association (APA)
Yang, Libo& Liu, Xuemei& Nie, Feiping& Liu, Mingtang. 2019. Large-Scale Spectral Clustering Based on Representative Points. Mathematical Problems in Engineering،Vol. 2019, no. 2019, pp.1-7.
https://search.emarefa.net/detail/BIM-1196250
Modern Language Association (MLA)
Yang, Libo…[et al.]. Large-Scale Spectral Clustering Based on Representative Points. Mathematical Problems in Engineering No. 2019 (2019), pp.1-7.
https://search.emarefa.net/detail/BIM-1196250
American Medical Association (AMA)
Yang, Libo& Liu, Xuemei& Nie, Feiping& Liu, Mingtang. Large-Scale Spectral Clustering Based on Representative Points. Mathematical Problems in Engineering. 2019. Vol. 2019, no. 2019, pp.1-7.
https://search.emarefa.net/detail/BIM-1196250
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1196250