  Kernel methods are extensively employed for nonlinear data clustering, yet
their effectiveness heavily relies on selecting suitable kernels and associated
parameters, posing challenges in advance determination. In response, Multiple
Kernel Clustering (MKC) has emerged as a solution, allowing the fusion of
information from multiple base kernels for clustering. However, both early
fusion and late fusion methods for large-scale MKC encounter challenges in
memory and time constraints, necessitating simultaneous optimization of both
aspects. To address this issue, we propose Efficient Multiple Kernel Concept
Factorization (EMKCF), which constructs a new sparse kernel matrix inspired by
local regression to achieve memory efficiency. EMKCF learns consensus and
individual representations by extending orthogonal concept factorization to
handle multiple kernels for time efficiency. Experimental results demonstrate
the efficiency and effectiveness of EMKCF on benchmark datasets compared to
state-of-the-art methods. The proposed method offers a straightforward,
scalable, and effective solution for large-scale MKC tasks.
