mlpack
2.2.5

This class computes SVD using incomplete incremental batch learning, as described in the following paper: More...
Public Member Functions  
SVDIncompleteIncrementalLearning (double u=0.001, double kw=0, double kh=0)  
Initialize the parameters of SVDIncompleteIncrementalLearning. 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 &, const size_t) 
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...  
Detailed Description
This class computes SVD using incomplete incremental batch learning, as described in the following paper:
This class implements 'Algorithm 2' as given in the paper. Incremental learning modifies only some feature values in W and H after scanning part of the input matrix (V). This differs from batch learning, which considers every element in V for each update of W and H. The regularization technique is also different: in incomplete incremental learning, regularization takes into account the number of elements in a given column of V.
 See also
 SVDBatchLearning
Definition at line 43 of file svd_incomplete_incremental_learning.hpp.
Constructor & Destructor Documentation
◆ SVDIncompleteIncrementalLearning()

inline 
Initialize the parameters of SVDIncompleteIncrementalLearning.
 Parameters

u Step value used in batch learning. kw Regularization constant for W matrix. kh Regularization constant for H matrix.
Definition at line 53 of file svd_incomplete_incremental_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 119 of file svd_incomplete_incremental_learning.hpp.
◆ Initialize()

inline 
Initialize parameters before factorization.
This function must be called before a new factorization. This simply sets the column being considered to 0, so the input matrix and rank are not used.
 Parameters

dataset Input matrix to be factorized. rank rank of factorization
Definition at line 70 of file svd_incomplete_incremental_learning.hpp.
◆ 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 86 of file svd_incomplete_incremental_learning.hpp.
The documentation for this class was generated from the following file:
 src/mlpack/methods/amf/update_rules/svd_incomplete_incremental_learning.hpp
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