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Multiple kernel clustering (MKC) algorithms usually learn an optimal kernel from a group of pre-specified base kernels to improve the clustering performance. However, we observe that existing MKC algorithms do not well handle the situation that kernels are corrupted with noise and outliers. In this paper, we first propose a novel method to learn an optimal consensus kernel from a group of pre-specified...
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...
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