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dc.contributor.authorZhou, L
dc.contributor.authorBai, X
dc.contributor.authorWang, D
dc.contributor.authorLiu, X
dc.contributor.authorZhou, J
dc.contributor.authorHancock, E
dc.date.accessioned2020-04-02T01:42:31Z
dc.date.available2020-04-02T01:42:31Z
dc.date.issued2019
dc.identifier.isbn9780999241141
dc.identifier.issn1045-0823
dc.identifier.doi10.24963/ijcai.2019/617
dc.identifier.urihttp://hdl.handle.net/10072/392914
dc.description.abstractSubspace clustering is a useful technique for many computer vision applications in which the intrinsic dimension of high-dimensional data is smaller than the ambient dimension. Traditional subspace clustering methods often rely on the self-expressiveness property, which has proven effective for linear subspace clustering. However, they perform unsatisfactorily on real data with complex nonlinear subspaces. More recently, deep autoencoder based subspace clustering methods have achieved success owning to the more powerful representation extracted by the autoencoder network. Unfortunately, these methods only considering the reconstruction of original input data can hardly guarantee the latent representation for the data distributed in subspaces, which inevitably limits the performance in practice. In this paper, we propose a novel deep subspace clustering method based on a latent distribution-preserving autoencoder, which introduces a distribution consistency loss to guide the learning of distribution-preserving latent representation, and consequently enables strong capacity of characterizing the real-world data for subspace clustering. Experimental results on several public databases show that our method achieves significant improvement compared with the state-of-the-art subspace clustering methods.
dc.description.peerreviewedYes
dc.publisherInternational Joint Conferences on Artificial Intelligence
dc.relation.ispartofconferencename28th International Joint Conference on Artificial Intelligence (IJCAI 2019)
dc.relation.ispartofconferencetitleProceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19)
dc.relation.ispartofdatefrom2019-08-10
dc.relation.ispartofdateto2019-08-16
dc.relation.ispartoflocationMacao, China
dc.relation.ispartofpagefrom4440
dc.relation.ispartofpageto4446
dc.relation.ispartofvolume2019-August
dc.subject.fieldofresearchArtificial Intelligence and Image Processing
dc.subject.fieldofresearchcode0801
dc.titleLatent distribution preserving deep subspace clustering
dc.typeConference output
dc.type.descriptionE1 - Conferences
dcterms.bibliographicCitationZhou, L; Bai, X; Wang, D; Liu, X; Zhou, J; Hancock, E, Latent distribution preserving deep subspace clustering, IJCAI International Joint Conference on Artificial Intelligence, 2019, 2019-August, pp. 4440-4446
dc.date.updated2020-04-02T01:39:04Z
dc.description.versionVersion of Record (VoR)
gro.rights.copyright© 2019 International Joint Conference on Artificial Intelligence. The attached file is reproduced here in accordance with the copyright policy of the publisher. Please refer to the Conference's website for access to the definitive, published version.
gro.hasfulltextFull Text
gro.griffith.authorZhou, Jun


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