Improving clustering by learning a bi-stochastic data similarity matrix

@article{Wang2011ImprovingCB,
  title={Improving clustering by learning a bi-stochastic data similarity matrix},
  author={Fei Wang and Ping Li and Arnd Christian K{\"o}nig and Muting Wan},
  journal={Knowledge and Information Systems},
  year={2011},
  volume={32},
  pages={351-382}
}
An idealized clustering algorithm seeks to learn a cluster-adjacency matrix such that, if two data points belong to the same cluster, the corresponding entry would be 1; otherwise, the entry would be 0. This integer (1/0) constraint makes it difficult to find the optimal solution. We propose a relaxation on the cluster-adjacency matrix, by deriving a bi-stochastic matrix from a data similarity (e.g., kernel) matrix according to the Bregman divergence. Our general method is named the Bregmanian… CONTINUE READING
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