A Novel Convex Clustering Method for High-Dimensional Data Using Semiproximal ADMM
Joint Authors
Chen, Huangyue
Li, Yan
Kong, Lingchen
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-09-21
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
Clustering is an important ingredient of unsupervised learning; classical clustering methods include K-means clustering and hierarchical clustering.
These methods may suffer from instability because of their tendency prone to sink into the local optimal solutions of the nonconvex optimization model.
In this paper, we propose a new convex clustering method for high-dimensional data based on the sparse group lasso penalty, which can simultaneously group observations and eliminate noninformative features.
In this method, the number of clusters can be learned from the data instead of being given in advance as a parameter.
We theoretically prove that the proposed method has desirable statistical properties, including a finite sample error bound and feature screening consistency.
Furthermore, the semiproximal alternating direction method of multipliers is designed to solve the sparse group lasso convex clustering model, and its convergence analysis is established without any conditions.
Finally, the effectiveness of the proposed method is thoroughly demonstrated through simulated experiments and real applications.
American Psychological Association (APA)
Chen, Huangyue& Kong, Lingchen& Li, Yan. 2020. A Novel Convex Clustering Method for High-Dimensional Data Using Semiproximal ADMM. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1202071
Modern Language Association (MLA)
Chen, Huangyue…[et al.]. A Novel Convex Clustering Method for High-Dimensional Data Using Semiproximal ADMM. Mathematical Problems in Engineering No. 2020 (2020), pp.1-12.
https://search.emarefa.net/detail/BIM-1202071
American Medical Association (AMA)
Chen, Huangyue& Kong, Lingchen& Li, Yan. A Novel Convex Clustering Method for High-Dimensional Data Using Semiproximal ADMM. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1202071
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1202071