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dc.contributor.authorPham, DucSon
dc.contributor.authorArandjelovic, O.
dc.contributor.authorVenkatesh, S.
dc.date.accessioned2017-03-15T22:16:48Z
dc.date.available2017-03-15T22:16:48Z
dc.date.created2017-02-26T19:31:36Z
dc.date.issued2016
dc.identifier.citationPham, D. and Arandjelovic, O. and Venkatesh, S. 2016. Achieving stable subspace clustering by post-processing generic clustering results, Proceedings of International Joint Conference on Neural Networks, 24-29 July 2016, pp. 2390-2396.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/49930
dc.identifier.doi10.1109/IJCNN.2016.7727496
dc.description.abstract

We propose an effective subspace selection scheme as a post-processing step to improve results obtained by sparse subspace clustering (SSC). Our method starts by the computation of stable subspaces using a novel random sampling scheme. Thus constructed preliminary subspaces are used to identify the initially incorrectly clustered data points and then to reassign them to more suitable clusters based on their goodness-of-fit to the preliminary model. To improve the robustness of the algorithm, we use a dominant nearest subspace classification scheme that controls the level of sensitivity against reassignment. We demonstrate that our algorithm is convergent and superior to the direct application of a generic alternative such as principal component analysis. On several popular datasets for motion segmentation and face clustering pervasively used in the sparse subspace clustering literature the proposed method is shown to reduce greatly the incidence of clustering errors while introducing negligible disturbance to the data points already correctly clustered.

dc.titleAchieving stable subspace clustering by post-processing generic clustering results
dc.typeConference Paper
dcterms.source.volume2016-October
dcterms.source.startPage2390
dcterms.source.endPage2396
dcterms.source.titleInternational Joint Conference on Neural Networks (IJCNN)
dcterms.source.seriesProceedings of the International Joint Conference on Neural Networks
dcterms.source.isbn9781509006199
curtin.departmentDepartment of Computing
curtin.accessStatusFulltext not available


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