Tensorlized Multi-Kernel Clustering via Consensus Tensor Decomposition

Abstract

Multi-kernel clustering aims to learn a fused kernel from a set of base kernels. However, conventional multi-kernel clustering methods typically suffer from inherent limitations in exploiting the interrelations and complementarity between the kernels. The noises and redundant information from original base kernels also lead to contamination of the fused kernel. To address these issues, this paper presents a Tensorlized Multi-Kernel Clustering (TensorMKC) method. The proposed TensorMKC stacks kernel matrices into a kernel tensor along the kernel space. To attain consensus extraction while mitigating the impact of noise, we incorporate the tensor low-rank constraint into the process of learning base kernels. Subsequently, a tensor-based weighted fusion strategy is employed to integrate the refined base kernels, yielding an optimized fused kernel for clustering. The process of kernel learning is formulated as a joint minimization problem to seek the promising fusion solution. Through extensive comparative experiments with fifteen popular methods on ten benchmark datasets from various fields, the results demonstrate that TensorMKC exhibits superior performance.