$ nca -h
Neighborhood Components Analysis (NCA)

  This program implements Neighborhood Components Analysis, both a linear
  dimensionality reduction technique and a distance learning technique.  The
  method seeks to improve k-nearest-neighbor classification on a dataset by
  scaling the dimensions.  The method is nonparametric, and does not require a
  value of k.  It works by using stochastic ("soft") neighbor assignments and
  using optimization techniques over the gradient of the accuracy of the
  neighbor assignments.

  For more details, see the following published paper:

  @inproceedings{
    author = {Goldberger, Jacob and Roweis, Sam and Hinton, Geoff and
        Salakhutdinov, Ruslan},
    booktitle = {Advances in Neural Information Processing Systems 17},
    pages = {513--520},
    publisher = {MIT Press},
    title = {{Neighbourhood Components Analysis}},
    year = {2004}
  }

  To work, this algorithm needs labeled data.  It can be given as the last row
  of the input dataset (--input_file), or alternatively in a separate file
  (--labels_file).

  ...

$ nca -h
Neighborhood Components Analysis (NCA)

  This program implements Neighborhood Components Analysis, both a linear
  dimensionality reduction technique and a distance learning technique.  The
  method seeks to improve k-nearest-neighbor classification on a dataset by
  scaling the dimensions.  The method is nonparametric, and does not require a
  value of k.  It works by using stochastic ("soft") neighbor assignments and
  using optimization techniques over the gradient of the accuracy of the
  neighbor assignments.

  For more details, see the following published paper:

  Goldberger, J., Roweis, S., Hinton, G., and Salakhutdinov, R.  "Neighbourhood
  Components Analysis", pp. 513-520, Advances in Neural Information Processing Systems
  17.  MIT Press, 2004.

  To work, this algorithm needs labeled data.  It can be given as the last row
  of the input dataset (--input_file), or alternatively in a separate file
  (--labels_file).

  ...