Augmented sparse representation for incomplete multiview clustering
dc.cclicence | N/A | en |
dc.contributor.author | Chen, Jie | |
dc.contributor.author | Yang, Shengxiang | |
dc.contributor.author | Peng, Xi | |
dc.contributor.author | Peng, Dezhong | |
dc.contributor.author | Wang, Zhu | |
dc.date.acceptance | 2022-08-22 | |
dc.date.accessioned | 2022-09-14T09:48:37Z | |
dc.date.available | 2022-09-14T09:48:37Z | |
dc.date.issued | 2022-09-07 | |
dc.description | The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link | en |
dc.description.abstract | Incomplete multiview data are collected from multiple sources or characterized by multiple modalities, where the features of some samples or some views may be missing. Incomplete multiview clustering aims to partition the data into different groups by taking full advantage of the complementary information from multiple incomplete views. Most existing methods based on matrix factorization or subspace learning attempt to recover the missing views or perform imputation of the missing features to improve clustering performance. However, this problem is intractable due to a lack of prior knowledge, e.g., label information or data distribution, especially when the missing views or features are completely damaged. In this paper, we proposed an augmented sparse representation (ASR) method for incomplete multiview clustering. We first introduce a discriminative sparse representation learning (DSRL) model, which learns the sparse representations of multiple views as applied to measure the similarity of the existing features. The DSRL model explores complementary and consistent information by integrating the sparse regularization item and a consensus regularization item, respectively. Simultaneously, it learns a discriminative dictionary from the original samples. The sparsity constrained optimization problem in the DSRL model can be efficiently solved by the alternating direction method of multipliers. Then, we present a similarity fusion scheme, namely, a sparsity augmented fusion of sparse representations, to obtain a sparsity augmented similarity matrix across different views for spectral clustering. Experimental results on several datasets demonstrate the effectiveness of the proposed ASR method for incomplete multiview clustering. | en |
dc.funder | Other external funder (please detail below) | en |
dc.funder.other | National Natural Science Foundation of China | en |
dc.identifier.citation | Chen, J., Yang, S., Peng, X., Peng, D. and Wang. Z (2022) Augmented sparse representation for incomplete multiview clustering. IEEE Transactions on Neural Networks and Learning Systems | en |
dc.identifier.doi | https://doi.org/10.1109/TNNLS.2022.3201699 | |
dc.identifier.issn | 2162-2388 | |
dc.identifier.uri | https://hdl.handle.net/2086/22153 | |
dc.language.iso | en_US | en |
dc.peerreviewed | Yes | en |
dc.projectid | 61303015 | en |
dc.publisher | IEEE | en |
dc.researchinstitute | Institute of Artificial Intelligence (IAI) | en |
dc.subject | Incomplete multiview data | en |
dc.subject | multiview clustering | en |
dc.subject | sparse representation | en |
dc.subject | similarity fusion | en |
dc.title | Augmented sparse representation for incomplete multiview clustering | en |
dc.type | Article | en |