Provided by: mlpack-bin_4.1.0-1ubuntu1_amd64 
NAME
mlpack_nca - neighborhood components analysis (nca)
SYNOPSIS
mlpack_nca -i unknown [-A double] [-b int] [-l unknown] [-L bool] [-n int] [-T int] [-M double] [-m double] [-N bool] [-B int] [-O string] [-s int] [-a double] [-t double] [-V bool] [-w double] [-o unknown] [-h -v]
DESCRIPTION
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.
To work, this algorithm needs labeled data. It can be given as the last row of the input
dataset (specified with '--input_file (-i)'), or alternatively as a separate matrix
(specified with '--labels_file (-l)').
This implementation of NCA uses stochastic gradient descent, mini-batch stochastic
gradient descent, or the L_BFGS optimizer. These optimizers do not guarantee global
convergence for a nonconvex objective function (NCA's objective function is nonconvex), so
the final results could depend on the random seed or other optimizer parameters.
Stochastic gradient descent, specified by the value 'sgd' for the parameter ’--optimizer
(-O)', depends primarily on three parameters: the step size (specified with '--step_size
(-a)'), the batch size (specified with ’--batch_size (-b)'), and the maximum number of
iterations (specified with ’--max_iterations (-n)'). In addition, a normalized starting
point can be used by specifying the '--normalize (-N)' parameter, which is necessary if
many warnings of the form 'Denominator of p_i is 0!' are given. Tuning the step size can
be a tedious affair. In general, the step size is too large if the objective is not mostly
uniformly decreasing, or if zero-valued denominator warnings are being issued. The step
size is too small if the objective is changing very slowly. Setting the termination
condition can be done easily once a good step size parameter is found; either increase the
maximum iterations to a large number and allow SGD to find a minimum, or set the maximum
iterations to 0 (allowing infinite iterations) and set the tolerance (specified by
'--tolerance (-t)') to define the maximum allowed difference between objectives for SGD to
terminate. Be careful---setting the tolerance instead of the maximum iterations can take a
very long time and may actually never converge due to the properties of the SGD optimizer.
Note that a single iteration of SGD refers to a single point, so to take a single pass
over the dataset, set the value of the '--max_iterations (-n)' parameter equal to the
number of points in the dataset.
The L-BFGS optimizer, specified by the value 'lbfgs' for the parameter ’--optimizer (-O)',
uses a back-tracking line search algorithm to minimize a function. The following
parameters are used by L-BFGS: '--num_basis (-B)' (specifies the number of memory points
used by L-BFGS), '--max_iterations (-n)', '--armijo_constant (-A)', '--wolfe (-w)',
'--tolerance (-t)' (the optimization is terminated when the gradient norm is below this
value), ’--max_line_search_trials (-T)', '--min_step (-m)', and '--max_step (-M)' (which
both refer to the line search routine). For more details on the L-BFGS optimizer, consult
either the mlpack L-BFGS documentation (in lbfgs.hpp) or the vast set of published
literature on L-BFGS.
By default, the SGD optimizer is used.
REQUIRED INPUT OPTIONS
--input_file (-i) [unknown]
Input dataset to run NCA on.
OPTIONAL INPUT OPTIONS
--armijo_constant (-A) [double]
Armijo constant for L-BFGS. Default value 0.0001.
--batch_size (-b) [int]
Batch size for mini-batch SGD. Default value 50.
--help (-h) [bool]
Default help info.
--info [string]
Print help on a specific option. Default value ''. --labels_file (-l) [unknown]
Labels for input dataset.
--linear_scan (-L) [bool]
Don't shuffle the order in which data points are visited for SGD or mini-batch SGD.
--max_iterations (-n) [int]
Maximum number of iterations for SGD or L-BFGS (0 indicates no limit). Default
value 500000.
--max_line_search_trials (-T) [int]
Maximum number of line search trials for L-BFGS. Default value 50.
--max_step (-M) [double]
Maximum step of line search for L-BFGS. Default value 1e+20.
--min_step (-m) [double]
Minimum step of line search for L-BFGS. Default value 1e-20.
--normalize (-N) [bool]
Use a normalized starting point for optimization. This is useful for when points
are far apart, or when SGD is returning NaN.
--num_basis (-B) [int]
Number of memory points to be stored for L-BFGS. Default value 5.
--optimizer (-O) [string]
Optimizer to use; 'sgd' or 'lbfgs'. Default value 'sgd'.
--seed (-s) [int]
Random seed. If 0, 'std::time(NULL)' is used. Default value 0.
--step_size (-a) [double]
Step size for stochastic gradient descent (alpha). Default value 0.01.
--tolerance (-t) [double]
Maximum tolerance for termination of SGD or L-BFGS. Default value 1e-07.
--verbose (-v) [bool]
Display informational messages and the full list of parameters and timers at the
end of execution.
--version (-V) [bool]
Display the version of mlpack.
--wolfe (-w) [double]
Wolfe condition parameter for L-BFGS. Default value 0.9.
OPTIONAL OUTPUT OPTIONS
--output_file (-o) [unknown] Output matrix for learned distance matrix.
ADDITIONAL INFORMATION
For further information, including relevant papers, citations, and theory, consult the
documentation found at http://www.mlpack.org or included with your distribution of mlpack.