Multi-kernel spectral clustering: Entrywise eigenvector perturbation bounds and exact recovery
Kernel spectral clustering with a single bandwidth can be inadequate for data exhibiting multiple characteristic pairwise-distance scales, a problem particularly prevalent in the high-dimensional regime. We address this issue through a multi-kernel formulation that aggregates kernels with different bandwidths. The bandwidths are selected as prescribed empirical quantiles of the pairwise squared distances, thereby capturing the relevant distance scales without requiring prior population-scale information. We develop a rigorous theoretical analysis of the resulting method under a general high-dimensional, multi-scale mixture model with heterogeneous cluster centers and covariance geometries...
| Authors | Zeqin Lin, Guangming Pan, Zhixiang Zhang et al. |
|---|---|
| Primary category | stat.ML |
| Published | 9 August 2026 |
| Last updated at source | 9 August 2026 |
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