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Publication Detail
Semi-supervised learning using an unsupervised atlas
  • Publication Type:
    Journal article
  • Publication Sub Type:
    Conference Proceeding
  • Authors:
    Pitelis N, Russell C, Agapito L
  • Publication date:
    01/01/2014
  • Pagination:
    565, 580
  • Journal:
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
  • Volume:
    8725 LNAI
  • Issue:
    PART 2
  • Status:
    Published
  • Print ISSN:
    0302-9743
Abstract
In many machine learning problems, high-dimensional datasets often lie on or near manifolds of locally low-rank. This knowledge can be exploited to avoid the "curse of dimensionality" when learning a classifier. Explicit manifold learning formulations such as lle are rarely used for this purpose, and instead classifiers may make use of methods such as local co-ordinate coding or auto-encoders to implicitly characterise the manifold. We propose novel manifold-based kernels for semi-supervised and supervised learning. We show how smooth classifiers can be learnt from existing descriptions of manifolds that characterise the manifold as a set of piecewise affine charts, or an atlas. We experimentally validate the importance of this smoothness vs. the more natural piecewise smooth classifiers, and we show a significant improvement over competing methods on standard datasets. In the semi-supervised learning setting our experiments show how using unlabelled data to learn the detailed shape of the underlying manifold substantially improves the accuracy of a classifier trained on limited labelled data. © 2014 Springer-Verlag.
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