Non-redundant Multi-view Clustering via Orthogonalization

@article{Cui2007NonredundantMC,
  title={Non-redundant Multi-view Clustering via Orthogonalization},
  author={Ying Cui and Xiaoli Z. Fern and Jennifer G. Dy},
  journal={Seventh IEEE International Conference on Data Mining (ICDM 2007)},
  year={2007},
  pages={133-142}
}
Typical clustering algorithms output a single clustering of the data. However, in real world applications, data can often be interpreted in many different ways; data can have different groupings that are reasonable and interesting from different perspectives. This is especially true for high-dimensional data, where different feature subspaces may reveal different structures of the data. Why commit to one clustering solution while all these alternative clustering views might be interesting to… CONTINUE READING

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