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Tensor Completion-Based Incomplete Multiview Clustering
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AI
DOI：10.1109/TCYB.2021.3140068.png)
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Abstract
En 中文
Incomplete multiview clustering is a challenging problem in the domain of unsupervised learning. However, the existing incomplete multiview clustering methods only consider the similarity structure of intraview while neglecting the similarity structure of interview. Thus, they cannot take advantage of both the complementary information and spatial structure embedded in similarity matrices of different views. To this end, we complete the incomplete graph with missing data referring to tensor complete and present a novel and effective model to handel the incomplete multiview clustering task. To be specific, we consider the similarity of the interview graphs via the tensor Schatten p-norm-based completion technique to make use of both the complementary information and spatial structure. Meanwhile, we employ the connectivity constraint for similarity matrices of different views such that the connected components approximately represent clusters. Thus, the learned entire graph not only has the low-rank structure but also well characterizes the relationship between unmissing data. Extensive experiments show the promising performance of the proposed method comparing with several incomplete multiview approaches in the clustering tasks.
Keywords:
Tensors
Interviews
Task analysis
Kernel
Optimization
Clustering methods
Clustering algorithms
Incomplete data
multiview clustering
tensor completion
Journal
IF:
10.5
Papers:
1.1W
Citations:
5.0W
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Cited Papers
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