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dc.contributor.authorZhou, L
dc.contributor.authorBai, X
dc.contributor.authorZhang, L
dc.contributor.authorZhou, J
dc.contributor.authorHancock, E
dc.date.accessioned2021-08-05T05:24:46Z
dc.date.available2021-08-05T05:24:46Z
dc.date.issued2020
dc.identifier.isbn9781728188089
dc.identifier.issn1051-4651
dc.identifier.doi10.1109/ICPR48806.2021.9412287
dc.identifier.urihttp://hdl.handle.net/10072/406624
dc.description.abstractSubspace clustering is a useful technique for many computer vision applications in which the intrinsic dimension of high-dimensional data is often smaller than the ambient dimension. Spectral clustering, as one of the main approaches to subspace clustering, often takes on a sparse representation or a low-rank representation to learn a block diagonal self-representation matrix for subspace generation. However, existing methods require solving a large scale convex optimization problem with a large set of data, with computational complexity reaches O(N3) for N data points. Therefore, the efficiency and scalability of traditional spectral clustering methods can not be guaranteed for large scale datasets. In this paper, we propose a subspace clustering model based on the Kronecker product. Due to the property that the Kronecker product of a block diagonal matrix with any other matrix is still a block diagonal matrix, we can efficiently learn the representation matrix which is formed by the Kronecker product of k smaller matrices. By doing so, our model significantly reduces the computational complexity to O(kN3/k). Furthermore, our model is general in nature, and can be adapted to different regularization based subspace clustering methods. Experimental results on two public datasets show that our model significantly improves the efficiency compared with several state-of-the-art methods. Moreover, we have conducted experiments on synthetic data to verify the scalability of our model for large scale datasets.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherIEEE
dc.publisher.placeOnline
dc.relation.ispartofconferencename2020 25th International Conference on Pattern Recognition (ICPR)
dc.relation.ispartofconferencetitleProceedings of ICPR 2020 25th International Conference on Pattern Recognition
dc.relation.ispartofdatefrom2021-01-10
dc.relation.ispartofdateto2021-01-15
dc.relation.ispartoflocationMilan, Italy
dc.relation.ispartofpagefrom1558
dc.relation.ispartofpageto1565
dc.subject.fieldofresearchInformation systems
dc.subject.fieldofresearchcode4609
dc.titleFast subspace clustering based on the Kronecker product
dc.typeConference output
dc.type.descriptionE1 - Conferences
dcterms.bibliographicCitationZhou, L; Bai, X; Zhang, L; Zhou, J; Hancock, E, Fast subspace clustering based on the Kronecker product, Proceedings of ICPR 2020 25th International Conference on Pattern Recognition, 2020, pp. 1558-1565
dc.date.updated2021-08-05T00:04:21Z
dc.description.versionAccepted Manuscript (AM)
gro.rights.copyright© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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gro.griffith.authorZhou, Jun


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