Joint diversity regularization and graph regularization for multiple kernel k means clustering via latent variables
Multiple kernel k means (MKKM) clustering algorithm is widely used in many machine learning and computer vision tasks. This algorithm improves clustering performance by extending the traditional kernel k means (KKM) clustering algorithm to a multiple setting by combining a group of pre specified kernels. In this paper, we develop and propose a multiple kernel k means clustering via latent variables (MKKLV) algorithm, in which base kernels can be adaptively adjusted with respect to each sample. To improve the effectiveness of the kernel specific and sample specific characteristics of the data, joint diversity regularization and graph regularization are utilized in the MKKLV algorithm. An efficient three step iterative algorithm is employed to jointly optimize the kernel specific and sample specific coefficients. Experiments validate that our algorithm outperforms state of the art techniques on several different benchmark datasets.
Neurocomputing