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

    Unsupervised manifold alignment using soft-assign technique

    Access Status
    Fulltext not available
    Authors
    Fan, K.
    Mian, A.
    Liu, Wan-Quan
    Li, L.
    Date
    2016
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Fan, K. and Mian, A. and Liu, W. and Li, L. 2016. Unsupervised manifold alignment using soft-assign technique. Machine Vision and Applications. 27 (6): pp. 929-942.
    Source Title
    Machine Vision and Applications
    DOI
    10.1007/s00138-016-0772-8
    ISSN
    0932-8092
    School
    Department of Computing
    URI
    http://hdl.handle.net/20.500.11937/40434
    Collection
    • Curtin Research Publications
    Abstract

    © 2016, Springer-Verlag Berlin Heidelberg. In this paper, we propose a robust unsupervised algorithm for automatic alignment of two manifolds in different datasets with possibly different dimensionalities. The significant contribution is that the proposed alignment algorithm is performed automatically without any assumptions on the correspondences between the two manifolds. For such purpose, we first automatically extract local feature histograms at each point of the manifolds and establish an initial similarity between the two datasets by matching their histogram-based features. Based on such similarity, an embedding space is estimated where the distance between the two manifolds is minimized while maximally retaining the original structure of the manifolds. The elegance of this idea is that such complicated problem is formulated as a generalized eigenvalue problem, which can be easily solved. The alignment process is achieved by iteratively increasing the sparsity of correspondence matrix until the two manifolds are correctly aligned and consequently one can reveal their joint structure. We demonstrate the effectiveness of our algorithm on different datasets by aligning protein structures, 3D face models and facial images of different subjects under pose and lighting variations. Finally, we also compare with a state-of-the-art algorithm and the results show the superiority of the proposed manifold alignment in terms of vision effect and numerical accuracy.

    Related items

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

    • Unsupervised Iterative Manifold Alignment via Local Feature Histograms
      Fan, Ke; Mian, A.; Liu, Wan-Quan; Li, Ling (2014)
      We propose a new unsupervised algorithm for the automatic alignment of two manifolds of different datasets with possibly different dimensionalities. Alignment is performed automatically without any assumptions on the ...
    • Detecting intrinsic loops underlying data manifold
      Meng, D.; Leung, Yee-Hong; Xu, Z. (2013)
      Detecting intrinsic loop structures of a data manifold is the necessary prestep for the proper employment of the manifold learning techniques and of fundamental importance in the discovery of the essential representational ...
    • Multilinear analysis of face image ensembles
      Rana, Santu (2010)
      Machine based face recognition is an important area of research that has attracted significant attention over the past few decades. Recently, multilinear models of face images have gained prominence as an alternative ...
    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.