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Multi-view Transformation Learning

  • Zhengming Ding
  • Handong Zhao
  • Yun Fu
Chapter
Part of the Advanced Information and Knowledge Processing book series (AI&KP)

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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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Indiana University-Purdue University IndianapolisIndianapolisUSA
  2. 2.Adobe ResearchSan JoseUSA
  3. 3.Northeastern UniversityBostonUSA

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