NCA< MetricType, OptimizerType > Class Template Reference

An implementation of Neighborhood Components Analysis, both a linear dimensionality reduction technique and a distance learning technique. More...

Public Member Functions
	NCA (const arma::mat &dataset, const arma::Row< size_t > &labels, MetricType metric=MetricType())
	Construct the Neighborhood Components Analysis object. More...
const arma::mat &	Dataset () const
	Get the dataset reference. More...
const arma::Row< size_t > &	Labels () const
	Get the labels reference. More...
void	LearnDistance (arma::mat &outputMatrix)
	Perform Neighborhood Components Analysis. More...
const OptimizerType &	Optimizer () const
	Get the optimizer. More...
OptimizerType &	Optimizer ()

Detailed Description

template

<

typename

MetricType

=

metric::SquaredEuclideanDistance

,

typename

OptimizerType

=

ens::StandardSGD

>

class mlpack::nca::NCA< MetricType, OptimizerType >

An implementation of 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{Goldberger2004,
  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}
}

Definition at line 49 of file nca.hpp.

Constructor & Destructor Documentation

NCA()

NCA	(	const arma::mat &	dataset,
		const arma::Row< size_t > &	labels,
		MetricType	metric = MetricType()
	)

Construct the Neighborhood Components Analysis object.

This simply stores the reference to the dataset and labels as well as the parameters for optimization before the actual optimization is performed.

Parameters

dataset	Input dataset.
labels	Input dataset labels.
stepSize	Step size for stochastic gradient descent.
maxIterations	Maximum iterations for stochastic gradient descent.
tolerance	Tolerance for termination of stochastic gradient descent.
shuffle	Whether or not to shuffle the dataset during SGD.
metric	Instantiated metric to use.

Member Function Documentation

Dataset()

const arma::mat& Dataset ( ) const

inline

Get the dataset reference.

Definition at line 81 of file nca.hpp.

Labels()

const arma::Row<size_t>& Labels ( ) const

inline

Get the labels reference.

Definition at line 83 of file nca.hpp.

LearnDistance()

void LearnDistance

(

arma::mat &

outputMatrix

)

Perform Neighborhood Components Analysis.

The output distance learning matrix is written into the passed reference. If LearnDistance() is called with an outputMatrix which has the correct size (dataset.n_rows x dataset.n_rows), that matrix will be used as the starting point for optimization.

Parameters

output_matrix

Covariance matrix of Mahalanobis distance.

Optimizer() [1/2]

const OptimizerType& Optimizer ( ) const

inline

Get the optimizer.

Definition at line 86 of file nca.hpp.

Optimizer() [2/2]

OptimizerType& Optimizer ( )

inline

Definition at line 87 of file nca.hpp.

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The documentation for this class was generated from the following file:

• /home/jenkins-mlpack/mlpack.org/_src/mlpack-3.2.1/src/mlpack/methods/nca/nca.hpp