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...  
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.
Definition at line 43 of file svd_incomplete_incremental_learning.hpp.

inline 
Initialize the parameters of SVDIncompleteIncrementalLearning.
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.

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.
V  Input matrix to be factorized. 
W  Basis matrix. 
H  Encoding matrix to be updated. 
Definition at line 121 of file svd_incomplete_incremental_learning.hpp.

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.
*  (dataset) Input matrix to be factorized. 
*  (rank) of factorization 
Definition at line 70 of file svd_incomplete_incremental_learning.hpp.

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.
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.