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Multi-view Transformation Learning

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Learning Representation for Multi-View Data Analysis

Part of the book series: Advanced Information and Knowledge Processing ((AI&KP))

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Abstract

In this chapter, we would propose two multi-view transformation learning algorithms to solve the classification problem. First of all, we consider the multi-view data have two kinds of manifold structures, i.e., class structure and view structure, then design a dual low-rank decomposition algorithm. Secondly, we assume the domain divergence involves more than one dominant factors, e.g., different view-points, various resolutions and changing illuminations, and explore an intermediate domain could often be found to build a bridge across them to facilitate the learning problem. After that, we propose a Coupled Marginalized Denoising Auto-encoders framework to address the cross-domain problem.

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Notes

  1. 1.

    http://vasc.ri.cmu.edu/idb/html/face/.

  2. 2.

    http://www.cs.columbia.edu/CAVE/software/softlib/coil-100.php.

  3. 3.

    http://www.eecs.qmul.ac.uk/~rlayne/downloads_qmul_elf_descriptor.html.

  4. 4.

    http://www.ee.cuhk.edu.hk/~rzhao/.

  5. 5.

    http://www1.ece.neu.edu/~yunfu/research/Kinface/Kinface.htm.

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Authors and Affiliations

  1. Indiana University-Purdue University Indianapolis, Indianapolis, IN, USA

    Zhengming Ding

  2. Adobe Research, San Jose, CA, USA

    Handong Zhao

  3. Northeastern University, Boston, MA, USA

    Yun Fu

Authors
  1. Zhengming Ding
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  2. Handong Zhao
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  3. Yun Fu
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Correspondence to Zhengming Ding .

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Ding, Z., Zhao, H., Fu, Y. (2019). Multi-view Transformation Learning. In: Learning Representation for Multi-View Data Analysis. Advanced Information and Knowledge Processing. Springer, Cham. https://doi.org/10.1007/978-3-030-00734-8_5

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  • DOI: https://doi.org/10.1007/978-3-030-00734-8_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00733-1

  • Online ISBN: 978-3-030-00734-8

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