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

Technical Program

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
Virtual Presentation  Click here to watch in the Virtual Conference
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.