Tensor-based Graph Learning Framework for Multi-view Clustering

Khái niệm cốt lõi

Proposing a novel tensor-based graph learning framework for multi-view clustering that considers consistency and specificity.

Tóm tắt

The content introduces a novel tensor-based graph learning framework for multi-view clustering. It addresses the limitations of existing methods by focusing on consistency and specificity in graph learning. The proposed method utilizes the Stiefel manifold for similarity distance measurement and formulates a tensor graph fusion framework. Experimental results demonstrate superior performance over state-of-the-art methods.

Thống kê

Experiments on real-world datasets have shown superior performance.
The proposed method achieves a perfect score of 100% in ACC, NMI, ARI, and Fscore on the HW dataset.
The accuracy of the proposed method on the 3-sources dataset is 77.57%.
The proposed method achieves an accuracy of 76.97% on the Yale dataset.
The proposed method achieves an accuracy of 77.33% on the WebKB dataset.

Trích dẫn

"We propose a novel tensor-based graph learning method, namely Tensor-based Graph Learning with Consistency and Specificity (CSTGL)."
"Experiments on real-world datasets have demonstrated that CSTGL outperforms some state-of-art multi-view clustering methods."

Thông tin chi tiết chính được chắt lọc từ

Tensor-based Graph Learning with Consistency and Specificity for Multi-view Clustering

by Long Shi,Lei... lúc arxiv.org 03-28-2024

https://arxiv.org/pdf/2403.18393.pdf

Yêu cầu sâu hơn

How does the utilization of the Stiefel manifold for similarity distance measurement impact the overall performance of the proposed method

The utilization of the Stiefel manifold for similarity distance measurement in the proposed method has a significant impact on its overall performance. By measuring similarity distance on the Stiefel manifold, the method can more accurately capture the intrinsic structure among data points. This approach allows for a more precise reflection of the underlying data structure compared to traditional methods that rely on Euclidean distance. The Stiefel distance measure considers the orthogonal properties on the manifold, providing a more accurate representation of similarity between data points. This leads to improved clustering performance by better preserving the intrinsic structure of complex data, ultimately enhancing the quality of the clustering results.

What are the potential implications of considering both consistency and specificity in graph learning for multi-view clustering

Considering both consistency and specificity in graph learning for multi-view clustering has several potential implications. By incorporating both consistency and specificity, the method can achieve a more comprehensive understanding of the data. Consistency ensures that shared information among different views is captured effectively, leading to a more robust and reliable clustering outcome. On the other hand, specificity allows for the preservation of unique characteristics and information present in each view, enhancing the diversity and richness of the clustering results. This dual approach enables the method to balance between capturing common patterns across views and retaining view-specific details, resulting in a more nuanced and accurate clustering solution.

How might the incorporation of tensor singular value decomposition enhance the understanding of high-order correlations in multi-view data

The incorporation of tensor singular value decomposition (t-SVD) enhances the understanding of high-order correlations in multi-view data in several ways. t-SVD is particularly effective in uncovering complex relationships and patterns that exist in high-dimensional data. By applying t-SVD, the method can capture high-order correlations among different views, allowing for a more comprehensive analysis of the data structure. This approach enables the model to extract latent features and relationships that may not be apparent in lower-order analyses. t-SVD facilitates the exploration of intricate data interactions and dependencies, leading to a more thorough understanding of the underlying data patterns and improving the clustering performance by leveraging high-order information effectively.