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Domain Adaptation Using the Grassmann Manifold

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Riemannian Computing in Computer Vision

Abstract

Modern data analysis is flush with large databases and high-dimensional inference problems. Often the size of these problems directly relates to the fact that given data may have variations that can be difficult to incorporate into well-known, classical methods. One of these sources of variation is that of differing data sources, often called domain adaptation. Many domain adaptation techniques use the notion of a shared representation to attempt to remove domain-specific variation in given observations. One way to obtain these representations is through dimension reduction using a linear projection onto a lower-dimensional subspace of the predictors. Estimating linear projections is intrinsically linked with parameters lying on the Grassmannian. We present some historical approaches and their relationship to recent advances in domain adaptation that exploit the Grassmannian through subspace estimation.

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Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA

    David A. Shaw

  2. Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, USA

    Rama Chellappa

Authors
  1. David A. Shaw
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  2. Rama Chellappa
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Correspondence to David A. Shaw .

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Editors and Affiliations

  1. Arizona State University, Tempe, Arizona, USA

    Pavan K. Turaga

  2. Florida State University, Tallahassee, Florida, USA

    Anuj Srivastava

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Shaw, D.A., Chellappa, R. (2016). Domain Adaptation Using the Grassmann Manifold. In: Turaga, P., Srivastava, A. (eds) Riemannian Computing in Computer Vision. Springer, Cham. https://doi.org/10.1007/978-3-319-22957-7_15

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  • DOI: https://doi.org/10.1007/978-3-319-22957-7_15

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22956-0

  • Online ISBN: 978-3-319-22957-7

  • eBook Packages: EngineeringEngineering (R0)

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