Please use this identifier to cite or link to this item:
http://bura.brunel.ac.uk/handle/2438/22900
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Du, G | - |
dc.contributor.author | Zhou, L | - |
dc.contributor.author | Yang, Y | - |
dc.contributor.author | Lu, K | - |
dc.contributor.author | Wang, L | - |
dc.date.accessioned | 2021-06-27T15:24:21Z | - |
dc.date.available | 2021-06-27T15:24:21Z | - |
dc.date.issued | 2021-05-08 | - |
dc.identifier.citation | Du, G., Zhou, L., Yang, Y. and . (2021) 'Deep Multiple Auto-Encoder-Based Multi-view Clustering'. Data Science Engineering, 6, pp. 323-338 doi: 10.1007/s41019-021-00159-z. | en_US |
dc.identifier.issn | 2364-1185 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/22900 | - |
dc.description.abstract | © The Author(s) 2021. Multi-view clustering (MVC), which aims to explore the underlying structure of data by leveraging heterogeneous information of different views, has brought along a growth of attention. Multi-view clustering algorithms based on different theories have been proposed and extended in various applications. However, most existing MVC algorithms are shallow models, which learn structure information of multi-view data by mapping multi-view data to low-dimensional representation space directly, ignoring the nonlinear structure information hidden in each view, and thus, the performance of multi-view clustering is weakened to a certain extent. In this paper, we propose a deep multi-view clustering algorithm based on multiple auto-encoder, termed MVC-MAE, to cluster multi-view data. MVC-MAE adopts auto-encoder to capture the nonlinear structure information of each view in a layer-wise manner and incorporate the local invariance within each view and consistent as well as complementary information between any two views together. Besides, we integrate the representation learning and clustering into a unified framework, such that two tasks can be jointly optimized. Extensive experiments on six real-world datasets demonstrate the promising performance of our algorithm compared with 15 baseline algorithms in terms of two evaluation metrics. | en_US |
dc.description.sponsorship | National Natural Science Foundation of China; Program for Innovation Research Team (University of Yunnan Province); National Social Science Foundation of China | en_US |
dc.format.extent | 323 - 338 | - |
dc.format.medium | Print-Electronic | - |
dc.language | English | - |
dc.language.iso | en | en_US |
dc.publisher | Springer Nature | en_US |
dc.rights | © The Author(s) 2021. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/. | - |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | - |
dc.subject | multi-view clustering | en_US |
dc.subject | auto-encoder | en_US |
dc.subject | complementary information | en_US |
dc.subject | consistent information | en_US |
dc.subject | local geometrical information | en_US |
dc.title | Deep Multiple Auto-Encoder based Multi-view Clustering | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.1007/s41019-021-00159-z | - |
dc.relation.isPartOf | Data Science and Engineering | - |
pubs.publication-status | Published | - |
pubs.volume | 6 | - |
dc.identifier.eissn | 2364-1541 | - |
Appears in Collections: | Brunel Business School Research Papers |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
FullText.pdf | 3.82 MB | Adobe PDF | View/Open |
This item is licensed under a Creative Commons License