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One-Class Multiple Instance Learning via Robust PCA for Common Object Discovery

  • Xinggang Wang
  • Zhengdong Zhang
  • Yi Ma
  • Xiang Bai
  • Wenyu Liu
  • Zhuowen Tu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7724)

Abstract

Principal component analysis (PCA), as a key component in statistical learning, has been adopted in a wide variety of applications in computer vision and machine learning. From a different angle, weakly supervised learning, more specifically multiple instance learning (MIL), allows fine-grained information to be exploited from coarsely-grained label information. In this paper, we propose an algorithm using the robust PCA (RPCA) [1] in a iterative way to perform simultaneous common object discovery and model learning under a one-class multiple instance learning setting. We show the advantage of our method on common object discovery and model learning, which needs no fine/coarse alignment in the input data; in addition, it achieves comparable results with standard two-class MIL learning algorithms but our method is learning from one-class data only.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Xinggang Wang
    • 1
  • Zhengdong Zhang
    • 2
  • Yi Ma
    • 2
  • Xiang Bai
    • 1
  • Wenyu Liu
    • 1
  • Zhuowen Tu
    • 2
    • 3
  1. 1.Huazhong University of Science and TechnologyChina
  2. 2.Visual Computing GroupMicrosoft Research AsiaChina
  3. 3.Lab of Neuro Imaging and Department of Computer ScienceUCLAUSA

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