2021 IEEE International Conference on Acoustics, Speech and Signal Processing
6-11 June 2021 • Toronto, Ontario, Canada
Extracting Knowledge from Information
| Paper ID | MLSP-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 | ||
| Session | MLSP-16: ML and Graphs | ||
| Location | Gather.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. | ||