mlpack
2.2.5

This class implements SVD batch learning with momentum. More...
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  HUpdate (const MatType &V, const arma::mat &W, arma::mat &H) 
The update rule for the encoding matrix H. More...  
template < typename MatType >  
void  Initialize (const MatType &dataset, const size_t rank) 
Initialize parameters before factorization. More...  
template < typename Archive >  
void  Serialize (Archive &ar, const unsigned int) 
Serialize the SVDBatch object. 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...  
Detailed Description
This class implements SVD batch learning with momentum.
This procedure is described in the following paper:
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.
Definition at line 41 of file svd_batch_learning.hpp.
Constructor & Destructor Documentation
◆ SVDBatchLearning()

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
Definition at line 52 of file svd_batch_learning.hpp.
Member Function Documentation
◆ HUpdate()

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.
Definition at line 133 of file svd_batch_learning.hpp.
◆ Initialize()

inline 
Initialize parameters before factorization.
This function must be called before a new factorization. This resets the internallyheld momentum.
 Parameters

dataset Input matrix to be factorized. rank rank of factorization
Definition at line 69 of file svd_batch_learning.hpp.
◆ Serialize()

inline 
Serialize the SVDBatch object.
Definition at line 169 of file svd_batch_learning.hpp.
References mlpack::data::CreateNVP().
◆ WUpdate()

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.
Definition at line 88 of file svd_batch_learning.hpp.
The documentation for this class was generated from the following file:
 src/mlpack/methods/amf/update_rules/svd_batch_learning.hpp
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