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    Achieving stable subspace clustering by post-processing generic clustering results

    Access Status
    Fulltext not available
    Authors
    Pham, DucSon
    Arandjelovic, O.
    Venkatesh, S.
    Date
    2016
    Type
    Conference Paper
    
    Metadata
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    Citation
    Pham, 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.
    Source Title
    International Joint Conference on Neural Networks (IJCNN)
    DOI
    10.1109/IJCNN.2016.7727496
    ISBN
    9781509006199
    School
    Department of Computing
    URI
    http://hdl.handle.net/20.500.11937/49930
    Collection
    • Curtin Research Publications
    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.

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