Optimized Data Fusion for Kernel k-Means Clustering

@article{Yu2012OptimizedDF,
  title={Optimized Data Fusion for Kernel k-Means Clustering},
  author={Shi Yu and L{\'e}on-Charles Tranchevent and Xinhai Liu and Wolfgang Gl{\"a}nzel and Johan A. K. Suykens and Bart De Moor and Yves Moreau},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2012},
  volume={34},
  pages={1031-1039}
}
This paper presents a novel optimized kernel k-means algorithm (OKKC) to combine multiple data sources for clustering analysis. The algorithm uses an alternating minimization framework to optimize the cluster membership and kernel coefficients as a nonconvex problem. In the proposed algorithm, the problem to optimize the cluster membership and the problem to optimize the kernel coefficients are all based on the same Rayleigh quotient objective; therefore the proposed algorithm converges locally… CONTINUE READING
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