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Self-representation Subspace Clustering for Incomplete Multi-view Data (IMSR)

Matalb implementation for ACM Multimedia paper:

Introduction

Abstract

Incomplete multi-view clustering is an important research topic in multimedia where partial data entries of one or more views are missing. Current subspace clustering approaches mostly employ matrix factorization on the observed feature matrices to address this issue. Meanwhile, self-representation technique is left unexplored, since it explicitly relies on full data entries to construct the coefficient matrix, which is contradictory to the incomplete data setting. However, it is widely observed that self-representation subspace method enjoys a better clustering performance over the factorization based one. Therefore, we adapt it to incomplete data by jointly performing data imputation and self-representation learning. To the best of our knowledge, this is the first attempt in incomplete multi-view clustering literature. Besides, the proposed method is carefully compared with current advances in experiment with respect to different missing ratios, verifying its effectiveness.

Citation

If you find our code useful, please cite:

@inproceedings{conf/acmmm/liuimsr21,
  author    = {Jiyuan Liu and
               Xinwang Liu and
               Yi Zhang and
               Pei Zhang and
               Wenxuan Tu and
               Siwei Wang and
               Sihang Zhou and
               Weixuan Liang and
               Siqi Wang and
               Yuexiang Yang},
  title     = {Self-representation Subspace Clustering for Incomplete Multi-view Data},
  journal   = {ACM International Conference on Multimedia (ACMMM)},
  volume    = {},
  pages     = {},
  year      = {2021},
  url       = {},
  doi       = {}
}

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