Multi-view low rank sparse representation method for three-way clustering

During the past years, multi-view clustering algorithms have demonstrated satisfactory clustering results by fusing the multiple views of the dataset. Nowadays, the researches of dimensionality reduction and learning discriminative features from multi-view data have soared in the literatures. As for clustering, generating the suitable subspace of the high dimensional multi-view data is crucial to boost the clustering performance. In addition, the relationship between the original data and the clusters still remains uncovered. In this article, we design a new multi-view low rank sparse representation method based on three-way clustering to tackle these challenges, which derive the common consensus low dimensional representation from the multi-view data and further proceed to get the relationship between the data items and clusters. Specifically, we accomplish this goal by taking advantage of the low-rank and the sparse factor on the data representation matrix. The L-2,L-1 norm is imposed on error matrix to reduce the impact of noise contained in the data. Finally, a new objective function is constructed to preserve the consistency between the views by using the low-rank sparse representation technique. The weighted low-rank matrix is utilized to build the consensus low rank matrix. Then, the whole objective function is optimized by using the Augmented Lagrange's Multiplier algorithm. Further, to find the uncertain relationship between the data items and the clusters, we pursue the neighborhood based three-way clustering technique to reflect the data items into core and fringe regions. Experiments conducted on the real-world datasets show the superior performance of the proposed method compared with the state-of-the-art algorithms.

Automated summary

In this article, a novel multi-view low rank sparse representation method based on three-way clustering is proposed to address the challenges in multi-view data clustering and to reveal the relationship between data items and clusters. By utilizing the low-rank and sparse factor on the data representation matrix, a new objective function is constructed to preserve the consistency between the views. The proposed method outperforms state-of-the-art algorithms in real-world datasets.