Nonlinear adaptive distance metric learning for clustering

  title={Nonlinear adaptive distance metric learning for clustering},
  author={Jianhui Chen and Zheng Zhao and Jieping Ye and Huan Liu},
A good distance metric is crucial for many data mining tasks. To learn a metric in the unsupervised setting, most metric learning algorithms project observed data to a low-dimensional manifold, where geometric relationships such as pairwise distances are preserved. It can be extended to the nonlinear case by applying the kernel trick, which embeds the data into a feature space by specifying the kernel function that computes the dot products between data points in the feature space. In this… CONTINUE READING
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The MOSEK interior point optimizer for linear programming: an implementation of the homogeneous algorithm

  • E. D. Andersen, K. D. Andersen
  • T. T. H. Frenk, K. Roos and S. Zhang, editors…
  • 2000
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