Sep 11, 2019 · Abstract: Multiple kernel clustering (MKC) attracts considerable attention due to its competitive performance in unsupervised learning.

To address these issues, we propose an effective and efficient multiple kernel k-means clustering method termed Consensus Multiple Kernel Clustering with Late ...

ABSTRACT Multiple kernel clustering (MKC) attracts considerable attention due to its competitive performance in unsupervised learning.

This paper proposes an MKKM clustering with a novel, effective matrix-induced regularization to reduce such redundancy and enhance the diversity of the ...

Missing: Late | Show results with:Late

Bibliographic details on Consensus Multiple Kernel K-Means Clustering With Late Fusion Alignment and Matrix-Induced Regularization.

After obtaining the consensus partition matrix H, a a common k-means clustering algorithm is applied to obtain the final cluster assignments. In existing late ...

The work in [Liu et al., 2016] proposes a multiple kernel k-means clustering algorithm with matrix-induced reg- ularization to reduce the redundancy of the pre- ...

Multiple kernel clustering aims to seek an appropriate combination of base kernels to mine inherent non-linear information for optimal ...

Differently, late fusion based multiple kernel clustering strategy seeks to exploit the complementary information in kernel partition space to achieve consensus ...