The main objective of the Multiple Kernel k-Means (MKKM) algorithm is to
extract non-linear information and achieve optimal clustering by optimizing
base kernel matrices. Current methods enhance information diversity and reduce
redundancy by exploiting interdependencies among multiple kernels based on
correlations or dissimilarities. Nevertheless, relying solely on a single
metric, such as correlation or dissimilarity, to define kernel relationships
introduces bias and incomplete characterization. Consequently, this limitation
hinders efficient information extraction, ultimately compromising clustering
performance. To tackle this challenge, we introduce a novel method that
systematically integrates both kernel correlation and dissimilarity. Our
approach comprehensively captures kernel relationships, facilitating more
efficient classification information extraction and improving clustering
performance. By emphasizing the coherence between kernel correlation and
dissimilarity, our method offers a more objective and transparent strategy for
extracting non-linear information and significantly improving clustering
precision, supported by theoretical rationale. We assess the performance of our
algorithm on 13 challenging benchmark datasets, demonstrating its superiority
over contemporary state-of-the-art MKKM techniques.

通过综合使用核相关性和差异性，我们提出了一种新的方法来提取非线性信息和实现最优聚类，从而提高聚类的性能。