Unified Tensor Framework for Incomplete Multi-view Clustering and Missing-view Inferring

Authors

  • Jie Wen Shenzhen Key Laboratory of Visual Object Detection and Recognition, Harbin Institute of Technology, Shenzhen, Shenzhen 518055, China
  • Zheng Zhang Shenzhen Key Laboratory of Visual Object Detection and Recognition, Harbin Institute of Technology, Shenzhen, Shenzhen 518055, China Peng Cheng Laboratory, Shenzhen 518055, China
  • Zhao Zhang School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230006, China
  • Lei Zhu School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China
  • Lunke Fei School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China
  • Bob Zhang PAMI Research Group, Dept. of Computer and Information Science, University of Macau, Taipa, Macau
  • Yong Xu Shenzhen Key Laboratory of Visual Object Detection and Recognition, Harbin Institute of Technology, Shenzhen, Shenzhen 518055, China Peng Cheng Laboratory, Shenzhen 518055, China

Keywords:

Multi-instance/Multi-view Learning, Clustering, Unsupervised & Self-Supervised Learning, Representation Learning

Abstract

In this paper, we propose a novel method, referred to as incomplete multi-view tensor spectral clustering with missing-view inferring (IMVTSC-MVI) to address the challenging multi-view clustering problem with missing views. Different from the existing methods which commonly focus on exploring the certain information of the available views while ignoring both of the hidden information of the missing views and the intra-view information of data, IMVTSC-MVI seeks to recover the missing views and explore the full information of such recovered views and available views for data clustering. In particular, IMVTSC-MVI incorporates the feature space based missing-view inferring and manifold space based similarity graph learning into a unified framework. In such a way, IMVTSC-MVI allows these two learning tasks to facilitate each other and can well explore the hidden information of the missing views. Moreover, IMVTSC-MVI introduces the low-rank tensor constraint to capture the high-order correlations of multiple views. Experimental results on several datasets demonstrate the effectiveness of IMVTSC-MVI for incomplete multi-view clustering.

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Published

2021-05-18

How to Cite

Wen, J., Zhang, Z., Zhang, Z., Zhu, L., Fei, L., Zhang, B., & Xu, Y. (2021). Unified Tensor Framework for Incomplete Multi-view Clustering and Missing-view Inferring. Proceedings of the AAAI Conference on Artificial Intelligence, 35(11), 10273-10281. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17231

Issue

Section

AAAI Technical Track on Machine Learning IV