Cai Yiran, Che Hangjun, Guo Wei, Pan Baicheng, Leung Man-Fai
College of Electronic and Information Engineering, Southwest University, Chongqing, China.
College of Electronic and Information Engineering, Southwest University, Chongqing, China; Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Chongqing, China.
Neural Netw. 2025 Aug 13;193:107981. doi: 10.1016/j.neunet.2025.107981.
Incomplete Multi-View Clustering (IMVC) focuses on uncovering the consensus and complementary information present in datasets with multiple incomplete views. However, existing IMVC methods face several limitations. First, many approaches exhibit high computational complexity. Second, anchor misalignment across views remains a challenge. Third, high-order correlations among views are often overlooked. To address these challenges, the paper introduces a novel framework called Tensorized Anchor Alignment for Incomplete Multi-view Clustering (TAA-IMC). Specifically, the view-specific anchor graphs are constructed to reduce computational complexity while preserving the diversity of information among views. Then, to mitigate the issue of anchor misalignment, a binary alignment matrix is introduced, ensuring proper correspondence between anchors across different views. Moreover, the aligned anchor graphs are integrated into a tensor representation with a low-rank constraint, enabling the extraction of high-order correlation information. Finally, the proposed TAA-IMC is solved using an alternating update method, showcasing efficiency through memory and time complexity analyses. Extensive comparative experiments conducted on seven benchmark datasets validate the efficiency and superiority of TAA-IMC over state-of-the-art methods.
