Effective and Efficient Graph Learning for Multi-view Clustering

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Conflicts of Interest

Identified Weaknesses

Rating Explanation

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Paper Summary

Paperzilla title

Building a Better Bipartite Bridge for Multi-View Clustering

This paper proposes a new method for multi-view clustering that uses tensor Schatten p-norm minimization and bipartite graph learning. The method is shown to be effective and efficient, outperforming state-of-the-art methods on several benchmark datasets. The results demonstrate the importance of exploiting both spatial structure and complementary information in multi-view clustering.

Possible Conflicts of Interest

None identified

Identified Weaknesses

Limited experimental comparison

The experimental comparison is limited. The baselines in the experiments are not sufficient, especially those state-of-the-art multi-view clustering methods.

Highly depend on bipartite graph

The proposed method highly depends on the artificially constructed bipartite graphs. It reduces the flexibility in real-world applications.

Lack of theoretical analysis

The paper lacks theoretical analysis.

Rating Explanation

The paper presents a novel method for multi-view clustering based on tensor Schatten p-norm minimization and bipartite graph learning. It addresses the limitations of existing approaches by characterizing both the spatial structure and the complementary information embedded in different views, and proposes a scalable algorithm suitable for large-scale datasets. The experimental results are impressive, demonstrating superior performance over state-of-the-art methods. However, some limitations need to be addressed, such as the limited experimental comparison and the lack of theoretical analysis, which are taken into account for the rating

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