RegularizedSVD< OptimizerType > Class Template Reference

Regularized SVD is a matrix factorization technique that seeks to reduce the error on the training set, that is on the examples for which the ratings have been provided by the users. More...

Public Member Functions
	RegularizedSVD (const size_t iterations=10, const double alpha=0.01, const double lambda=0.02)
	Constructor for Regularized SVD. More...
void	Apply (const arma::mat &data, const size_t rank, arma::mat &u, arma::mat &v)
	Obtains the user and item matrices using the provided data and rank. More...

Detailed Description

template

<

typename

OptimizerType

=

ens::StandardSGD

>

class mlpack::svd::RegularizedSVD< OptimizerType >

Regularized SVD is a matrix factorization technique that seeks to reduce the error on the training set, that is on the examples for which the ratings have been provided by the users.

It is a fairly straightforward technique where the user and item matrices are updated with the help of Stochastic Gradient Descent(SGD) updates. The updates also penalize the learning of large feature values by means of regularization. More details can be found in the following links:

http://sifter.org/~simon/journal/20061211.html http://www.cs.uic.edu/~liub/KDD-cup-2007/proceedings/Regular-Paterek.pdf

An example of how to use the interface is shown below:

arma::mat data; // Rating data in the form of coordinate list.

const size_t rank = 20; // Rank used for the decomposition.
const size_t iterations = 10; // Number of iterations used for optimization.

const double alpha = 0.01 // Learning rate for the SGD optimizer.
const double lambda = 0.1 // Regularization parameter for the optimization.

// Make a RegularizedSVD object.
RegularizedSVD<> rSVD(iterations, alpha, lambda);

arma::mat u, v; // User and item matrices.

// Use the Apply() method to get a factorization.
rSVD.Apply(data, rank, u, v);

Definition at line 58 of file regularized_svd.hpp.

Constructor & Destructor Documentation

RegularizedSVD()

RegularizedSVD	(	const size_t	iterations = 10,
		const double	alpha = 0.01,
		const double	lambda = 0.02
	)

Constructor for Regularized SVD.

Obtains the user and item matrices after training on the passed data. The constructor initiates an object of class RegularizedSVDFunction for optimization. It uses the SGD optimizer by default. The optimizer uses a template specialization of Optimize().

Parameters

iterations	Number of optimization iterations.
alpha	Learning rate for the SGD optimizer.
lambda	Regularization parameter for the optimization.

Member Function Documentation

Apply()

void Apply	(	const arma::mat &	data,
		const size_t	rank,
		arma::mat &	u,
		arma::mat &	v
	)

Obtains the user and item matrices using the provided data and rank.

Parameters

data	Rating data matrix.
rank	Rank parameter to be used for optimization.
u	Item matrix obtained on decomposition.
v	User matrix obtained on decomposition.

Referenced by RegSVDPolicy::Apply().

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The documentation for this class was generated from the following file:

• /home/ryan/src/mlpack.org/_src/mlpack-git/src/mlpack/methods/regularized_svd/regularized_svd.hpp