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- research-articleJuly 2024
Kernel correlation–dissimilarity for Multiple Kernel k-Means clustering
AbstractThe 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 ...
Highlights- 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 ...