Convex optimization learning of faithful Euclidean distance representations in nonlinear dimensionality reduction

@article{Ding2017ConvexOL,
  title={Convex optimization learning of faithful Euclidean distance representations in nonlinear dimensionality reduction},
  author={Chao Ding and Hou-Duo Qi},
  journal={Math. Program.},
  year={2017},
  volume={164},
  pages={341-381}
}
Classical multidimensional scaling only works well when the noisy distances observed in a high dimensional space can be faithfully represented by Euclidean distances in a low dimensional space. Advanced models such as Maximum Variance Unfolding (MVU) and Minimum Volume Embedding (MVE) use Semi-Definite Programming (SDP) to reconstruct such faithful… CONTINUE READING