Kernel Correlation-Dissimilarity for Multiple Kernel k-Means Clustering

arXiv:2403.03448v1 Announce Type: new
Abstract: 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. …

abstract algorithm arxiv clustering correlation correlations cs.cv cs.lg current diversity extract information kernel k-means linear multiple non-linear reduce redundancy type