Non-negative Laplacian Embedding

@article{Luo2009NonnegativeLE,
  title={Non-negative Laplacian Embedding},
  author={Dijun Luo and Chris H. Q. Ding and Heng Huang and Tao Li},
  journal={2009 Ninth IEEE International Conference on Data Mining},
  year={2009},
  pages={337-346}
}
Laplacian embedding provides a low dimensional representation for a matrix of pairwise similarity data using the eigenvectors of the Laplacian matrix. The true power of Laplacian embedding is that it provides an approximation of the Ratio Cut clustering. However, Ratio Cut clustering requires the solution to be {\it nonnegative}. In this paper, we propose a new approach, nonnegative Laplacian embedding, which approximates Ratio Cut clustering in a more direct way than traditional approaches… CONTINUE READING

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Jordan . Convex and seminonnegative matrix factorizations

  • Chris Ding, Tao Li, Wei Peng, Haesun Park
  • IEEE Trans . Pattern Analysis and Machine…
  • 2009

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