Curtin University Homepage
  • Library
  • Help
    • Admin

    espace - Curtin’s institutional repository

    JavaScript is disabled for your browser. Some features of this site may not work without it.
    View Item 
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item

    Affinity learning via a diffusion process for subspace clustering

    Access Status
    Fulltext not available
    Authors
    Li, Q.
    Liu, Wan-Quan
    Li, Ling
    Date
    2018
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Li, Q. and Liu, W. and Li, L. 2018. Affinity learning via a diffusion process for subspace clustering. Pattern Recognition. 84: pp. 39-50.
    Source Title
    Pattern Recognition
    DOI
    10.1016/j.patcog.2018.07.002
    ISSN
    0031-3203
    School
    School of Electrical Engineering, Computing and Mathematical Science (EECMS)
    URI
    http://hdl.handle.net/20.500.11937/69801
    Collection
    • Curtin Research Publications
    Abstract

    Subspace clustering refers to the problem of finding low-dimensional subspaces (clusters) for high-dimensional data. Current state-of-the-art subspace clustering methods are usually based on spectral clustering, where an affinity matrix is learned by the self-expressive model, i.e., reconstructing every data point by a linear combination of all other points while regularizing the coefficients using the l1norm. The sparsity nature of l1norm guarantees the subspace-preserving property (i.e., no connection between clusters) of affinity matrix under certain condition, but the connectedness property (i.e., fully connected within clusters) is less considered. In this paper, we propose a novel affinity learning method by incorporating the sparse representation and diffusion process. Instead of using sparse coefficients directly as the affinity values, we apply the l1norm as a neighborhood selection criterion, which could capture the local manifold structure. An effective diffusion process is then deployed to spread such local information along with the global geometry of data manifold. Each pairwise affinity is augmented and re-evaluated by the context of data point pair, yielding significant enhancements of within-cluster connectivity. Extensive experiments on synthetic data and real-world data have demonstrated the effectiveness of the proposed method in comparison to other state-of-the-art methods.

    Related items

    Showing items related by title, author, creator and subject.

    • Multi-View Subspace Clustering for Face Images
      Zhang, X.; Phung, D.; Venkatesh, S.; Pham, DucSon; Liu, Wan-Quan (2016)
      In many real-world computer vision applications, such as multi-camera surveillance, the objects of interest are captured by visual sensors concurrently, resulting in multi-view data. These views usually provide complementary ...
    • Fuzzy based affinity learning for spectral clustering
      Li, Q.; Ren, Y.; Li, L.; Liu, Wan-Quan (2016)
      Spectral clustering makes use of spectral-graph structure of an affinity matrix to partition data into disjoint meaningful groups. It requires robust and appropriate affinity graphs as input in order to form clusters with ...
    • Unsupervised modeling of multiple data sources : a latent shared subspace approach
      Gupta, Sunil Kumar (2011)
      The growing number of information sources has given rise to joint analysis. While the research community has mainly focused on analyzing data from a single source, there has been relatively few attempts on jointly analyzing ...
    Advanced search

    Browse

    Communities & CollectionsIssue DateAuthorTitleSubjectDocument TypeThis CollectionIssue DateAuthorTitleSubjectDocument Type

    My Account

    Admin

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Follow Curtin

    • 
    • 
    • 
    • 
    • 

    CRICOS Provider Code: 00301JABN: 99 143 842 569TEQSA: PRV12158

    Copyright | Disclaimer | Privacy statement | Accessibility

    Curtin would like to pay respect to the Aboriginal and Torres Strait Islander members of our community by acknowledging the traditional owners of the land on which the Perth campus is located, the Whadjuk people of the Nyungar Nation; and on our Kalgoorlie campus, the Wongutha people of the North-Eastern Goldfields.