Enhancing Multi-View Clustering with Iterative Algorithm and Sparse Regularization

In this article, researchers explore a novel method for multi-view clustering called ELMSC, which stands for "Efficient Lower Matrix Spectral Clustering." The authors aim to address limitations in existing methods by incorporating multiple features and reducing computational complexity. They evaluate their approach on several real-world datasets and show that it outperforms established baselines in terms of clustering accuracy.
The authors begin by explaining the importance of multi-view clustering, which involves analyzing data from different perspectives to gain a more comprehensive understanding of the underlying patterns. They then provide an overview of existing methods, including LRRBestSV and Co-reg, which construct a representation matrix using nuclear norm or consensus adjacency matrices. These approaches have limitations, such as computational complexity and reliance on a single view.
To address these limitations, the authors propose ELMSC, which leverages a novel iterative algorithm based on Alternating Direction Methods (ADMM) to learn a low-rank representation matrix. This matrix captures the similarity between data points from different views and enables efficient clustering. The authors also introduce a sparse regularization term to prevent redundant computations and improve scalability.
The authors evaluate ELMSC on several datasets, including Reuters, Yale, and BBCSport. They compare their method with established baselines, such as LRRBestSV, AWP, MLRSSC, MCGC, CSMSC, MCLGF, and TBGL. The results show that ELMSC outperforms these methods in terms of clustering accuracy, with the highest performance on the BBCSport dataset.
The authors also analyze the contribution of different components of their method to the clustering performance. They find that the iterative algorithm based on ADMM and the sparse regularization term are the most important factors in improving clustering accuracy.
In conclusion, the authors demonstrate that ELMSC is a powerful method for multi-view clustering that outperforms existing approaches in terms of accuracy and efficiency. Its ability to handle multiple features and reduce computational complexity makes it a valuable tool for analyzing complex data sets.