mlpack::amf::SVDBatchLearning Class Reference

This class implements SVD batch learning with momentum. More...

#include <svd_batch_learning.hpp>

Public Member Functions
	SVDBatchLearning (double u=0.0002, double kw=0, double kh=0, double momentum=0.9)
	SVD Batch learning constructor. More...
template<typename MatType >
void	Initialize (const MatType &dataset, const size_t rank)
	Initialize parameters before factorization. More...
template<typename MatType >
void	WUpdate (const MatType &V, arma::mat &W, const arma::mat &H)
	The update rule for the basis matrix W. More...
template<typename MatType >
void	HUpdate (const MatType &V, const arma::mat &W, arma::mat &H)
	The update rule for the encoding matrix H. More...
template<typename Archive >
void	serialize (Archive &ar, const uint32_t)
	Serialize the SVDBatch object.

Detailed Description

This class implements SVD batch learning with momentum.

This procedure is described in the following paper:

@techreport{ma2008guide,
  title={A Guide to Singular Value Decomposition for Collaborative
      Filtering},
  author={Ma, Chih-Chao},
  year={2008},
  institution={Department of Computer Science, National Taiwan University}
}

This class implements 'Algorithm 4' as given in the paper.

The factorizer decomposes the matrix V into two matrices W and H such that sum of sum of squared error between V and W * H is minimum. This optimization is performed with gradient descent. To make gradient descent faster, momentum is added.

Constructor & Destructor Documentation

SVDBatchLearning()

mlpack::amf::SVDBatchLearning::SVDBatchLearning ( double  u = 0.0002, double  kw = 0, double  kh = 0, double  momentum = 0.9  )

inline

SVD Batch learning constructor.

Parameters

u	step value used in batch learning
kw	regularization constant for W matrix
kh	regularization constant for H matrix
momentum	momentum applied to batch learning process

Member Function Documentation

HUpdate()

template<typename MatType >

void mlpack::amf::SVDBatchLearning::HUpdate ( const MatType &  V, const arma::mat &  W, arma::mat &  H  )

inline

The update rule for the encoding matrix H.

The function takes in all the matrices and only changes the value of the H matrix.

Parameters

V	Input matrix to be factorized.
W	Basis matrix.
H	Encoding matrix to be updated.

Initialize()

template<typename MatType >

void mlpack::amf::SVDBatchLearning::Initialize ( const MatType &  dataset, const size_t  rank  )

inline

Initialize parameters before factorization.

This function must be called before a new factorization. This resets the internally-held momentum.

Parameters

dataset	Input matrix to be factorized.
rank	rank of factorization

WUpdate()

template<typename MatType >

void mlpack::amf::SVDBatchLearning::WUpdate ( const MatType &  V, arma::mat &  W, const arma::mat &  H  )

inline

The update rule for the basis matrix W.

The function takes in all the matrices and only changes the value of the W matrix.

Parameters

V	Input matrix to be factorized.
W	Basis matrix to be updated.
H	Encoding matrix.

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

• src/mlpack/methods/amf/update_rules/svd_batch_learning.hpp