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Consistent graph learning for multi-view spectral clustering
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AI
DOI：10.1016/j.patcog.2024.110598.png)
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摘要
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Given the heterogeneous information of multiple views and the possible noise embedded in multi -view data, it is difficult to directly learn a consistent representation from multiple graphs to depict the intrinsic structure of all views. We propose a consistent graph learning method by exploiting both the high -order correlations underlying multiple views and the global structure of each single view. First, we calculate a transition probability matrix from each view. Second, a tensor is constructed by stacking each transition matrix as its frontal slice and then decomposed into the latent and error tensors. For the latent tensor, after rotated, a weighted tensor nuclear norm is used to encourage the rotate tensor to fully exploit the high -order correlations underlying multiple views. Furthermore, each frontal slice of the latent tensor is regularized by the nuclear norm to capture the global structure of each single view, and is restricted by probability constraints. Besides, we adopt the Frobenius-norm-based regularization to directly learn a common affinity matrix from the latent tensor. The established model is readily optimized by the alternating Lagrangian method. Extensive experiments on six real world datasets demonstrate that our method outperforms state-of-the-art methods.
Keyword:
Spectral clustering
Multi-view clustering
Tensor learning
Graph learning
期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
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引用论文
Multi-view Spectral Clustering with Adaptive Graph Learning and Tensor Schatten p-norm基于自适应图学习和张量Schatten p-范数的多视图谱聚类.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
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