Augmented sparse representation for incomplete multiview clustering

dc.cclicenceN/Aen
dc.contributor.authorChen, Jie
dc.contributor.authorYang, Shengxiang
dc.contributor.authorPeng, Xi
dc.contributor.authorPeng, Dezhong
dc.contributor.authorWang, Zhu
dc.date.acceptance2022-08-22
dc.date.accessioned2022-09-14T09:48:37Z
dc.date.available2022-09-14T09:48:37Z
dc.date.issued2022-09-07
dc.descriptionThe 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 linken
dc.description.abstractIncomplete 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.funderOther external funder (please detail below)en
dc.funder.otherNational Natural Science Foundation of Chinaen
dc.identifier.citationChen, 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 Systemsen
dc.identifier.doihttps://doi.org/10.1109/TNNLS.2022.3201699
dc.identifier.issn2162-2388
dc.identifier.urihttps://hdl.handle.net/2086/22153
dc.language.isoen_USen
dc.peerreviewedYesen
dc.projectid61303015en
dc.publisherIEEEen
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en
dc.subjectIncomplete multiview dataen
dc.subjectmultiview clusteringen
dc.subjectsparse representationen
dc.subjectsimilarity fusionen
dc.titleAugmented sparse representation for incomplete multiview clusteringen
dc.typeArticleen

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