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mlpack::nca::NCA< MetricType, OptimizerType >(3) | MLPACK | mlpack::nca::NCA< MetricType, OptimizerType >(3) |
NAME¶
mlpack::nca::NCA< MetricType, OptimizerType > - An implementation of Neighborhood Components Analysis, both a linear dimensionality reduction technique and a distance learning technique.SYNOPSIS¶
Public Member Functions¶
NCA (const arma::mat &dataset, const arma::Col< size_t > & labels, MetricType metric=MetricType())
Private Attributes¶
const arma::mat & dataset
Detailed Description¶
template<typename MetricType = metric::SquaredEuclideanDistance, template< typename > class OptimizerType = optimization::SGD>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 59 of file nca.hpp.
Constructor & Destructor Documentation¶
template<typename MetricType = metric::SquaredEuclideanDistance, template< typename > class OptimizerType = optimization::SGD> mlpack::nca::NCA< MetricType, OptimizerType >:: NCA (const arma::mat &dataset, const arma::Col< size_t > &labels, MetricTypemetric = 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¶
template<typename MetricType = metric::SquaredEuclideanDistance, template< typename > class OptimizerType = optimization::SGD> const arma::mat& mlpack::nca::NCA< MetricType, OptimizerType >::Dataset () const [inline]¶
Get the dataset reference. Definition at line 91 of file nca.hpp. References mlpack::nca::NCA< MetricType, OptimizerType >::dataset.template<typename MetricType = metric::SquaredEuclideanDistance, template< typename > class OptimizerType = optimization::SGD> const arma::Col<size_t>& mlpack::nca::NCA< MetricType, OptimizerType >::Labels () const [inline]¶
Get the labels reference. Definition at line 93 of file nca.hpp. References mlpack::nca::NCA< MetricType, OptimizerType >::labels.template<typename MetricType = metric::SquaredEuclideanDistance, template< typename > class OptimizerType = optimization::SGD> void mlpack::nca::NCA< MetricType, OptimizerType >::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.
template<typename MetricType = metric::SquaredEuclideanDistance, template< typename > class OptimizerType = optimization::SGD> const OptimizerType< SoftmaxErrorFunction<MetricType> >& mlpack::nca::NCA< MetricType, OptimizerType >::Optimizer () const [inline]¶
Get the optimizer. Definition at line 96 of file nca.hpp. References mlpack::nca::NCA< MetricType, OptimizerType >::optimizer.template<typename MetricType = metric::SquaredEuclideanDistance, template< typename > class OptimizerType = optimization::SGD> OptimizerType< SoftmaxErrorFunction<MetricType> >& mlpack::nca::NCA< MetricType, OptimizerType >::Optimizer () [inline]¶
Definition at line 98 of file nca.hpp. References mlpack::nca::NCA< MetricType, OptimizerType >::optimizer.template<typename MetricType = metric::SquaredEuclideanDistance, template< typename > class OptimizerType = optimization::SGD> std::string mlpack::nca::NCA< MetricType, OptimizerType >::ToString () const¶
Member Data Documentation¶
template<typename MetricType = metric::SquaredEuclideanDistance, template< typename > class OptimizerType = optimization::SGD> const arma::mat& mlpack::nca::NCA< MetricType, OptimizerType >::dataset [private]¶
Dataset reference. Definition at line 106 of file nca.hpp. Referenced by mlpack::nca::NCA< MetricType, OptimizerType >::Dataset().template<typename MetricType = metric::SquaredEuclideanDistance, template< typename > class OptimizerType = optimization::SGD> SoftmaxErrorFunction<MetricType> mlpack::nca::NCA< MetricType, OptimizerType >::errorFunction [private]¶
The function to optimize. Definition at line 114 of file nca.hpp.template<typename MetricType = metric::SquaredEuclideanDistance, template< typename > class OptimizerType = optimization::SGD> const arma::Col<size_t>& mlpack::nca::NCA< MetricType, OptimizerType >::labels [private]¶
Labels reference. Definition at line 108 of file nca.hpp. Referenced by mlpack::nca::NCA< MetricType, OptimizerType >::Labels().template<typename MetricType = metric::SquaredEuclideanDistance, template< typename > class OptimizerType = optimization::SGD> MetricType mlpack::nca::NCA< MetricType, OptimizerType >::metric [private]¶
Metric to be used. Definition at line 111 of file nca.hpp.template<typename MetricType = metric::SquaredEuclideanDistance, template< typename > class OptimizerType = optimization::SGD> OptimizerType< SoftmaxErrorFunction<MetricType> > mlpack::nca::NCA< MetricType, OptimizerType >::optimizer [private]¶
The optimizer to use. Definition at line 117 of file nca.hpp. Referenced by mlpack::nca::NCA< MetricType, OptimizerType >::Optimizer().Author¶
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