Dimensionality reduction by unsupervised regression

  title={Dimensionality reduction by unsupervised regression},
  author={Miguel {\'A}. Carreira-Perpi{\~n}{\'a}n and Zhengdong Lu},
  journal={2008 IEEE Conference on Computer Vision and Pattern Recognition},
We consider the problem of dimensionality reduction, where given high-dimensional data we want to estimate two mappings: from high to low dimension (dimensionality reduction) and from low to high dimension (reconstruction). We adopt an unsupervised regression point of view by introducing the unknown low-dimensional coordinates of the data as parameters, and formulate a regularised objective functional of the mappings and low-dimensional coordinates. Alternating minimisation of this functional… CONTINUE READING
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