Kernel correlation–dissimilarity for Multiple Kernel k-Means clustering.

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. • We propose a MKKM method that assesses kernel correlation–dissimilarity consistency. • We utilize Manhattan distance and Frobenius inner product for kernel similarity. • Integrating these measures improves performance and generalization in clustering. • We employ the splitting method to iteratively update indicators and kernel weights. • Results on 13 challenging datasets confirm algorithm's effectiveness and convergence. [ABSTRACT FROM AUTHOR]