Late Fusion Multiple Kernel Clustering With Local Kernel Alignment Maximization

• Published in: IEEE transactions on multimedia 2023, Vol.25, p.993-1007
• Main Authors: Zhang, Tiejian, Liu, Xinwang, Gong, Lei, Wang, Siwei, Niu, Xin, Shen, Li
• Format: Article
• Language: English
• Subjects: Algorithms; Alignment; Benchmark testing; block diagonal structure; Clustering; Clustering algorithms; Iterative methods; Kernel; Kernels; local base partition; local kernel; Manifolds; Maximization; Multiple kernel clustering; neighbor; Optimization; Parameter sensitivity; Partitioning algorithms; Regularization; Robustness
• ISSN: 1520-9210; 1941-0077
• DOI: 10.1109/TMM.2021.3136094

Multi-view clustering, which appropriately integrates information from multiple sources to reveal data's inherent structure, is gaining traction in clustering. Though existing procedures have yielded satisfactory results, we observe that they have neglected the inherent local structure in the base kernels. This may cause adverse effects on clustering. To solve the problem, we introduce LF-MKC-LKA, a simple yet effective late fusion multiple kernel clustering with local kernel alignment maximisation approach. In particular, we first determine the nearest k neighbours in the average kernel space for each sample and record the information in the nearest neighbor indicator matrix. Then, the nearest neighbor indicator matrix can be used to generate local structure matrix of each sample. The local kernels of each view may then be generated using the local structure matrix, retaining just the highly confident local similarities for learning the intrinsic global manifold of data. They can also be utilised to keep the block diagonal structure and improve the robustness of the underlying kernels against noise.We input the local kernels of each view into the kernel k-means (KKM) algorithm and get the local base partitions. Finally, we use a three-step iterative optimization approach to maximize the alignment of the consensus partition using base partitions and a regularisation term. As demonstrated, a significant number of trials on 11 multi-kernel benchmark datasets have shown that the proposed LF-MKC-LKA is effective and efficient. A number of experiments are also designed to demonstrate the fast convergence, excellent performance, robustness and low parameter sensitivity of the algorithm. Our code can be find at https://github.com/TiejianZhang/TMM21-LF-MKC-LKA .