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Learning high-dimensional correspondence via manifold learning and local approximation

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Abstract

The recent years have witnessed a surge of interests of learning high-dimensional correspondence, which is important for both machine learning and neural computation community. Manifold learning–based researches have been considered as one of the most promising directions. In this paper, by analyzing traditional methods, we summarized a new framework for high-dimensional correspondence learning. Within this framework, we also presented a new approach, Local Approximation Maximum Variance Unfolding. Compared with other machine learning–based methods, it could achieve higher accuracy. Besides, we also introduce how to use the proposed framework and methods in a concrete application, cross-system personalization (CSP). Promising experimental results on image alignment and CSP applications are proposed for demonstration.

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Notes

  1. http://vasc.ri.cmu.edu/idb/html/motion/hand/.

  2. http://www.seas.up-enn.edu/jhham/.

  3. http://www.grouplens.org.

  4. http://goldberg.berkeley.edu/jester-data/.

  5. http://www.informatik.uni-freiburg.de/cziegler/BX/.

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Authors and Affiliations

  1. Department of Mathematics and Systems Science, National University of Defense Technology, Changsha, 410073, China

    Chenping Hou & Dongyun Yi

  2. Department of Computer Science and Engineering, University of Texas, Arlington, TX, 76019, USA

    Feiping Nie & Hua Wang

  3. State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation, Tsinghua University, Beijing, 100084, China

    Changshui Zhang

Authors
  1. Chenping Hou
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  2. Feiping Nie
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  3. Hua Wang
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  4. Dongyun Yi
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  5. Changshui Zhang
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Corresponding author

Correspondence to Chenping Hou.

Additional information

We thank the National Natural Science Foundation of China, under Grant No. 61005003, 60975038, for their supports.

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Hou, C., Nie, F., Wang, H. et al. Learning high-dimensional correspondence via manifold learning and local approximation. Neural Comput & Applic 24, 1555–1568 (2014). https://doi.org/10.1007/s00521-013-1369-z

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  • DOI: https://doi.org/10.1007/s00521-013-1369-z

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