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A Deep Fusion Gaussian Mixture Model for Multiview Land Data Clustering
Joint Authors
Xia, Feng
Li, Peng
Wang, Lu
Chen, Zhikui
Zhang, Jianing
Jin, Shan
Zhao, Wenhan
Gao, Jing
Source
Wireless Communications and Mobile Computing
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-10-17
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Information Technology and Computer Science
Abstract EN
With the rapid industrialization and urbanization, pattern mining of soil contamination of heavy metals is attracting increasing attention to control soil contamination.
However, the correlation over various heavy metals and the high-dimension representation of heavy metal data pose vast challenges on the accurate mining of patterns over heavy metals of soil contamination.
To solve those challenges, a multiview Gaussian mixture model is proposed in this paper, to naturally capture complicated relationships over multiviews on the basis of deep fusion features of data.
Specifically, a deep fusion feature architecture containing modality-specific and modality-common stacked autoencoders is designed to distill fusion representations from the information of all views.
Then, the Gaussian mixture model is extended on the fusion representations to naturally recognize the accurate patterns of the intra- and inter-views.
Finally, extensive experiments are conducted on the representative datasets to evaluate the performance of the multiview Gaussian mixture model.
Results show the outperformance of the proposed methods.
American Psychological Association (APA)
Li, Peng& Chen, Zhikui& Gao, Jing& Zhang, Jianing& Jin, Shan& Zhao, Wenhan…[et al.]. 2020. A Deep Fusion Gaussian Mixture Model for Multiview Land Data Clustering. Wireless Communications and Mobile Computing،Vol. 2020, no. 2020, pp.1-9.
https://search.emarefa.net/detail/BIM-1214850
Modern Language Association (MLA)
Li, Peng…[et al.]. A Deep Fusion Gaussian Mixture Model for Multiview Land Data Clustering. Wireless Communications and Mobile Computing No. 2020 (2020), pp.1-9.
https://search.emarefa.net/detail/BIM-1214850
American Medical Association (AMA)
Li, Peng& Chen, Zhikui& Gao, Jing& Zhang, Jianing& Jin, Shan& Zhao, Wenhan…[et al.]. A Deep Fusion Gaussian Mixture Model for Multiview Land Data Clustering. Wireless Communications and Mobile Computing. 2020. Vol. 2020, no. 2020, pp.1-9.
https://search.emarefa.net/detail/BIM-1214850
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1214850