2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
Login Paper Search My Schedule Paper Index Help

My ICASSP 2021 Schedule

Note: Your custom schedule will not be saved unless you create a new account or login to an existing account.
  1. Create a login based on your email (takes less than one minute)
  2. Perform 'Paper Search'
  3. Select papers that you desire to save in your personalized schedule
  4. Click on 'My Schedule' to see the current list of selected papers
  5. Click on 'Printable Version' to create a separate window suitable for printing (the header and menu will appear, but will not actually print)

Paper Detail

Paper IDMLSP-16.2
Paper Title INCOMPLETE MULTI-VIEW SUBSPACE CLUSTERING WITH LOW-RANK TENSOR
Authors Jianlun Liu, Shaohua Teng, Wei Zhang, Xiaozhao Fang, Lunke Fei, Zhuxiu Zhang, Guangdong University of Technology, China
SessionMLSP-16: ML and Graphs
LocationGather.Town
Session Time:Wednesday, 09 June, 14:00 - 14:45
Presentation Time:Wednesday, 09 June, 14:00 - 14:45
Presentation Poster
Topic Machine Learning for Signal Processing: [MLR-LMM] Learning from multimodal data
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Incomplete multi-view clustering has attracted increasing attentions due to its superiority in partitioning unlabeled multi-view data with missing instances in real application. However, most existing methods cannot fully exploit both the view-specific and cross-view relations among data points and ignore the high-order correlations across all views. To address these issues, we propose a novel Incomplete Multi-view Subspace Clustering with Low-rank Tensor (IMSCLT) method, which could be the first tensor-based incomplete multi-view clustering method to the best of our knowledge. Specifically, the subspace representations with low-rank tensor constraint are employed to exploit both the view-specific and cross-view relations among data points and capture the high-order correlations of multiple views simultaneously. In addition, we devise a novel module which can learn a discriminative similarity graph for multi-view learning task by approximating the inner product of the view-specific and common subspace representations. Augmented Lagrangian alternative direction minimization strategy is adopted to solve the proposed IMSCLT. The experiments on several benchmark datasets demonstrate the effectiveness of IMSCLT.