Efficient and Effective Multiple Kernel Clustering via Randomized Kernel Compression

Multiple kernel clustering (MKC) has attracted widespread attention in recent years. Its core advantage lies in its ability to effectively capture the complex nonlinear and complementary structures of data by adaptively combining multiple kernel functions. Although various MKC methods have achieved significant success, they generally rely on the storage and spectral decomposition of multiple $n\times n$ kernel matrices. This computational bottleneck limits the practical applicability of these methods when handling large-scale datasets. To address this, we propose a scalable multiple-kernel clustering framework based on symmetric randomized compression. Specifically, by compressing each base kernel into a low-dimensional core space, we retain its dominant spectral information while substantially reducing the cost of subsequent kernel fusion and spectral optimization. Furthermore, we construct a shared subspace to capture spectral structures consistently supported across multiple kernels, enabling kernel fusion and weight learning in a common representation space. Theoretically, we establish a reconstruction-error bound for each compressed base kernel and analyze how the shared subspace captures cross-kernel consensus structures. Experimental results on eight multi-view benchmark datasets show that the proposed method achieves significant improvements in clustering performance and computational efficiency, and exhibits notable scalability advantages in large-scale data scenarios.