Adaptive Distance Metric Learning for Clustering

  title={Adaptive Distance Metric Learning for Clustering},
  author={Jieping Ye and Zheng Zhao and Huan Liu},
  journal={2007 IEEE Conference on Computer Vision and Pattern Recognition},
A good distance metric is crucial for unsupervised learning from high-dimensional data. To learn a metric without any constraint or class label information, most unsupervised metric learning algorithms appeal to projecting observed data onto a low-dimensional manifold, where geometric relationships such as local or global pairwise distances are preserved. However, the projection may not necessarily improve the separability of the data, which is the desirable outcome of clustering. In this paper… CONTINUE READING
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