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| SVDBatchLearning (double u=0.0002, double kw=0, double kh=0, double momentum=0.9, double min=-DBL_MIN, double max=DBL_MAX) |
| SVD Batch learning constructor. More...
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template<typename MatType > |
void | HUpdate (const MatType &V, const arma::mat &W, arma::mat &H) |
| The update rule for the encoding matrix H. More...
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template<typename MatType > |
void | Initialize (const MatType &dataset, const size_t rank) |
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template<typename MatType > |
void | WUpdate (const MatType &V, arma::mat &W, const arma::mat &H) |
| The update rule for the basis matrix W. More...
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This class implements SVD batch learning with momentum.
This procedure is described in the paper 'A Guide to singular Value Decomposition' by Chih-Chao Ma. Class implements 'Algorithm 4' given in the paper. This 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 37 of file svd_batch_learning.hpp.
template<typename MatType >
void mlpack::amf::SVDBatchLearning::HUpdate |
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const MatType & |
V, |
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const arma::mat & |
W, |
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arma::mat & |
H |
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) |
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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
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V | Input matrix to be factorized. |
W | Basis matrix. |
H | Encoding matrix to be updated. |
Definition at line 117 of file svd_batch_learning.hpp.
References kh, mH, momentum, and u.
template<typename MatType >
void mlpack::amf::SVDBatchLearning::WUpdate |
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const MatType & |
V, |
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arma::mat & |
W, |
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const arma::mat & |
H |
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) |
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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
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V | Input matrix to be factorized. |
W | Basis matrix to be updated. |
H | Encoding matrix. |
Definition at line 77 of file svd_batch_learning.hpp.
References kw, momentum, mW, and u.