As one of the most important research topics in the unsupervised learning field, Multi-View Clustering (MVC) has been widely studied in the past decade and numerous MVC methods have been developed. Among these methods, the recently emerged Graph Neural Networks (GNN) shine a light on modeling both topological structure and node attributes in the form of graphs, to guide unified embedding learning and clustering. However, the effectiveness of existing GNN-based MVC methods is still limited due to the insufficient consideration in utilizing the self-supervised information and graph information, which can be reflected from the following two aspects: 1) most of these models merely use the self-supervised information to guide the feature learning and fail to realize that such information can be also applied in graph learning and sample weighting; 2) the usage of graph information is generally limited to the feature aggregation in these models, yet it also provides valuable evidence in detecting noisy samples. To this end, in this paper we propose Self-Supervised Graph Attention Networks for Deep Weighted Multi-View Clustering (SGDMC), which promotes the performance of GNN-based deep MVC models by making full use of the self-supervised information and graph information. Specifically, a novel attention-allocating approach that considers both the similarity of node attributes and the self-supervised information is developed to comprehensively evaluate the relevance among different nodes. Meanwhile, to alleviate the negative impact caused by noisy samples and the discrepancy of cluster structures, we further design a sample-weighting strategy based on the attention graph as well as the discrepancy between the global pseudo-labels and the local cluster assignment. Experimental results on multiple real-world datasets demonstrate the effectiveness of our method over existing approaches.
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This content will become publicly available on June 4, 2024
Inv-Senet: Invariant Self Expression Network for Clustering Under Biased Data
Subspace clustering algorithms are used for understanding the
cluster structure that explains the patterns prevalent in the dataset
well. These methods are extensively used for data-exploration tasks
in various areas of Natural Sciences. However, most of these methods fail to handle confounding attributes in the dataset. For datasets
where a data sample represent multiple attributes, naively applying
any clustering approach can result in undesired output. To this end,
we propose a novel framework for jointly removing confounding attributes while learning to cluster data points in individual subspaces.
Assuming we have label information about these confounding attributes, we regularize the clustering method by adversarially learning to minimize the mutual information between the data representation and the confounding attribute labels. Our experimental result
on synthetic and real-world datasets demonstrate the effectiveness of
our approach.
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- Award ID(s):
- 1947972
- NSF-PAR ID:
- 10444738
- Date Published:
- Journal Name:
- IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Page Range / eLocation ID:
- 1 to 5
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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