Multiview clustering method for view-unaligned data
A new challenge for multi-view learning was posed by corrupted view-correspondences.To address this issue， an effective multi-view learning method for view-unaligned data was proposed.First，to capture cross-view latent affinity in multi-view heterogenous feature spaces，representation learning was em...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Journal on Communications
2022-07-01
|
| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2022134/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | A new challenge for multi-view learning was posed by corrupted view-correspondences.To address this issue， an effective multi-view learning method for view-unaligned data was proposed.First，to capture cross-view latent affinity in multi-view heterogenous feature spaces，representation learning was employed based on multi-view non-negative matrix factorization to embed original features into a measurable low-dimensional subspace.Second, view-alignment relationships were modeled as optimal matching of a bipartite graph, which could be generalized to multiple-views situations via the proposed concept reference view.Representation learning and data alignment were further integrated into a unified Bi-level optimization framework to mutually boost the two learning processes, effectively enhancing the ability to learn from view-unaligned data.Extensive experimental results of view-unaligned clustering on three public datasets demonstrate that the proposed method outperforms eight advanced multiview clustering methods on multiple evaluation metrics. |
|---|---|
| ISSN: | 1000-436X |