Collaborative Embedding Learning via Tensor Integration for Multi-View Clustering

Multi-view clustering exploits the complementary information of different views for comprehensive data analysis. Recently, graph learning techniques with low-dimensional embedding have been developed to learn consensus affinity graph for multi-view clustering. However, projecting data into the low-dimensional space has often resulted in the compression of data information, which is insufficient for graph learning. To address this challenge, this paper proposes a Collaborative Embedding Learning via Tensor (CELT) method, which learns intra-view affinity graphs for each view from both the original space and the low-dimensional space jointly. Additionally, all intra-view affinity graphs are stacked into a tensor, allowing the learning of a consensus affinity to capture inter-view consistency. In this way, an enhanced consensus affinity is obtained to improve the performance of multi-view clustering. Extensive experimental results on eight real-world datasets demonstrate that the proposed collaborative learning framework is effective for graph learning and outperforms competitive multi-view clustering methods.