Alternating Matrix Factorization. More...

Classes
class	AMF
	This class implements AMF (alternating matrix factorization) on the given matrix V. More...

class	AverageInitialization
	This initialization rule initializes matrix W and H to root of the average of V, perturbed with uniform noise. More...

class	CompleteIncrementalTermination
	This class acts as a wrapper for basic termination policies to be used by SVDCompleteIncrementalLearning. More...

class	GivenInitialization
	This initialization rule for AMF simply fills the W and H matrices with the matrices given to the constructor of this object. More...

class	IncompleteIncrementalTermination
	This class acts as a wrapper for basic termination policies to be used by SVDIncompleteIncrementalLearning. More...

class	MaxIterationTermination
	This termination policy only terminates when the maximum number of iterations has been reached. More...

class	NMFALSUpdate
	This class implements a method titled 'Alternating Least Squares' described in the following paper: More...

class	NMFMultiplicativeDistanceUpdate
	The multiplicative distance update rules for matrices W and H. More...

class	NMFMultiplicativeDivergenceUpdate
	This follows a method described in the paper 'Algorithms for Non-negative. More...

class	RandomAcolInitialization
	This class initializes the W matrix of the AMF algorithm by averaging p randomly chosen columns of V. More...

class	RandomInitialization
	This initialization rule for AMF simply fills the W and H matrices with uniform random noise in [0, 1]. More...

class	SimpleResidueTermination
	This class implements a simple residue-based termination policy. More...

class	SimpleToleranceTermination
	This class implements residue tolerance termination policy. More...

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

class	SVDCompleteIncrementalLearning
	This class computes SVD using complete incremental batch learning, as described in the following paper: More...

class	SVDCompleteIncrementalLearning< arma::sp_mat >
	TODO : Merge this template specialized function for sparse matrix using common row_col_iterator. More...

class	SVDIncompleteIncrementalLearning
	This class computes SVD using incomplete incremental batch learning, as described in the following paper: More...

class	ValidationRMSETermination
	This class implements validation termination policy based on RMSE index. More...

Typedefs
typedef amf::AMF< amf::SimpleResidueTermination, amf::RandomAcolInitialization<>, amf::NMFALSUpdate >	NMFALSFactorizer

typedef amf::AMF< amf::SimpleResidueTermination, amf::RandomAcolInitialization<>, amf::SVDBatchLearning >	SparseSVDBatchFactorizer
	Add simple typedefs. More...

typedef amf::AMF< amf::SimpleResidueTermination, amf::RandomAcolInitialization<>, amf::SVDCompleteIncrementalLearning< arma::sp_mat > >	SparseSVDCompleteIncrementalFactorizer
	SparseSVDCompleteIncrementalFactorizer factorizes given sparse matrix V into two matrices W and H by complete incremental gradient descent. More...

typedef amf::AMF< amf::SimpleResidueTermination, amf::RandomAcolInitialization<>, amf::SVDIncompleteIncrementalLearning >	SparseSVDIncompleteIncrementalFactorizer
	SparseSVDIncompleteIncrementalFactorizer factorizes given sparse matrix V into two matrices W and H by incomplete incremental gradient descent. More...

typedef amf::AMF< amf::SimpleResidueTermination, amf::RandomAcolInitialization<>, amf::SVDBatchLearning >	SVDBatchFactorizer
	SparseSVDBatchFactorizer factorizes given matrix V into two matrices W and H by gradient descent. More...

typedef amf::AMF< amf::SimpleResidueTermination, amf::RandomAcolInitialization<>, amf::SVDCompleteIncrementalLearning< arma::mat > >	SVDCompleteIncrementalFactorizer
	SVDCompleteIncrementalFactorizer factorizes given matrix V into two matrices W and H by complete incremental gradient descent. More...

typedef amf::AMF< amf::SimpleResidueTermination, amf::RandomAcolInitialization<>, amf::SVDIncompleteIncrementalLearning >	SVDIncompleteIncrementalFactorizer
	SVDIncompleteIncrementalFactorizer factorizes given matrix V into two matrices W and H by incomplete incremental gradient descent. More...

Functions
template<>
void	SVDBatchLearning::HUpdate< arma::sp_mat > (const arma::sp_mat &V, const arma::mat &W, arma::mat &H)

template<>
void	SVDBatchLearning::WUpdate< arma::sp_mat > (const arma::sp_mat &V, arma::mat &W, const arma::mat &H)
	TODO : Merge this template specialized function for sparse matrix using common row_col_iterator. More...

template<>
void	SVDIncompleteIncrementalLearning::HUpdate< arma::sp_mat > (const arma::sp_mat &V, const arma::mat &W, arma::mat &H)

template<>
void	SVDIncompleteIncrementalLearning::WUpdate< arma::sp_mat > (const arma::sp_mat &V, arma::mat &W, const arma::mat &H)
	TODO : Merge this template specialized function for sparse matrix using common row_col_iterator. More...

Detailed Description

Alternating Matrix Factorization.

Typedef Documentation

typedef amf::AMF<amf::SimpleResidueTermination, amf::RandomAcolInitialization<>, amf::NMFALSUpdate> mlpack::amf::NMFALSFactorizer

Definition at line 143 of file amf.hpp.

typedef amf::AMF<amf::SimpleResidueTermination, amf::RandomAcolInitialization<>, amf::SVDBatchLearning> mlpack::amf::SparseSVDBatchFactorizer

Add simple typedefs.

SparseSVDBatchFactorizer factorizes given sparse matrix V into two matrices W and H by gradient descent. SVD batch learning is described in paper 'A Guide to singular Value Decomposition' by Chih-Chao Ma.

See also: SVDBatchLearning

Definition at line 199 of file amf.hpp.

typedef amf::AMF<amf::SimpleResidueTermination, amf::RandomAcolInitialization<>, amf::SVDCompleteIncrementalLearning<arma::sp_mat> > mlpack::amf::SparseSVDCompleteIncrementalFactorizer

SparseSVDCompleteIncrementalFactorizer factorizes given sparse matrix V into two matrices W and H by complete incremental gradient descent.

SVD complete incremental learning is described in paper 'A Guide to singular Value Decomposition' by Chih-Chao Ma.

See also: SVDCompleteIncrementalLearning

Definition at line 248 of file amf.hpp.

typedef amf::AMF<amf::SimpleResidueTermination, amf::RandomAcolInitialization<>, amf::SVDIncompleteIncrementalLearning> mlpack::amf::SparseSVDIncompleteIncrementalFactorizer

SparseSVDIncompleteIncrementalFactorizer factorizes given sparse matrix V into two matrices W and H by incomplete incremental gradient descent.

SVD incomplete incremental learning is described in paper 'A Guide to singular Value Decomposition' by Chih-Chao Ma.

See also: SVDIncompleteIncrementalLearning

Definition at line 222 of file amf.hpp.

typedef amf::AMF<amf::SimpleResidueTermination, amf::RandomAcolInitialization<>, amf::SVDBatchLearning> mlpack::amf::SVDBatchFactorizer

SparseSVDBatchFactorizer factorizes given matrix V into two matrices W and H by gradient descent.

SVD batch learning is described in paper 'A Guide to singular Value Decomposition' by Chih-Chao Ma.

See also: SVDBatchLearning

Definition at line 210 of file amf.hpp.

typedef amf::AMF<amf::SimpleResidueTermination, amf::RandomAcolInitialization<>, amf::SVDCompleteIncrementalLearning<arma::mat> > mlpack::amf::SVDCompleteIncrementalFactorizer

SVDCompleteIncrementalFactorizer factorizes given matrix V into two matrices W and H by complete incremental gradient descent.

SVD complete incremental learning is described in paper 'A Guide to singular Value Decomposition' by Chih-Chao Ma.

See also: SVDCompleteIncrementalLearning

Definition at line 261 of file amf.hpp.

typedef amf::AMF<amf::SimpleResidueTermination, amf::RandomAcolInitialization<>, amf::SVDIncompleteIncrementalLearning> mlpack::amf::SVDIncompleteIncrementalFactorizer

SVDIncompleteIncrementalFactorizer factorizes given matrix V into two matrices W and H by incomplete incremental gradient descent.

SVD incomplete incremental learning is described in paper 'A Guide to singular Value Decomposition' by Chih-Chao Ma.

See also: SVDIncompleteIncrementalLearning

Definition at line 235 of file amf.hpp.

Function Documentation

template<>

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

inline

Definition at line 231 of file svd_batch_learning.hpp.

References mlpack::amf::SVDBatchLearning::kh, mlpack::amf::SVDBatchLearning::mH, mlpack::amf::SVDBatchLearning::momentum, and mlpack::amf::SVDBatchLearning::u.

template<>

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

inline

TODO : Merge this template specialized function for sparse matrix using common row_col_iterator.

WUpdate function specialization for sparse matrix

Definition at line 203 of file svd_batch_learning.hpp.

References mlpack::amf::SVDBatchLearning::kw, mlpack::amf::SVDBatchLearning::momentum, mlpack::amf::SVDBatchLearning::mW, and mlpack::amf::SVDBatchLearning::u.

template<>

void mlpack::amf::SVDIncompleteIncrementalLearning::HUpdate< arma::sp_mat >	(	const arma::sp_mat &	V,
		const arma::mat &	W,
		arma::mat &	H
	)

inline

Definition at line 184 of file svd_incomplete_incremental_learning.hpp.

References mlpack::amf::SVDIncompleteIncrementalLearning::currentUserIndex, mlpack::amf::SVDIncompleteIncrementalLearning::kh, and mlpack::amf::SVDIncompleteIncrementalLearning::u.

template<>

void mlpack::amf::SVDIncompleteIncrementalLearning::WUpdate< arma::sp_mat >	(	const arma::sp_mat &	V,
		arma::mat &	W,
		const arma::mat &	H
	)

inline

TODO : Merge this template specialized function for sparse matrix using common row_col_iterator.

template specialiazed functions for sparse matrices

Definition at line 163 of file svd_incomplete_incremental_learning.hpp.

References mlpack::amf::SVDIncompleteIncrementalLearning::currentUserIndex, mlpack::amf::SVDIncompleteIncrementalLearning::kw, and mlpack::amf::SVDIncompleteIncrementalLearning::u.

Classes

Typedefs

Functions

Detailed Description

Typedef Documentation

Function Documentation