mlpack::svd Namespace Reference

Classes
class	BiasSVD
	Bias SVD is an improvement on Regularized SVD which is a matrix factorization techniques. More...
class	BiasSVDFunction
	This class contains methods which are used to calculate the cost of BiasSVD's objective function, to calculate gradient of parameters with respect to the objective function, etc. More...
class	QUIC_SVD
	QUIC-SVD is a matrix factorization technique, which operates in a subspace such that A's approximation in that subspace has minimum error(A being the data matrix). More...
class	RandomizedBlockKrylovSVD
	Randomized block krylov SVD is a matrix factorization that is based on randomized matrix approximation techniques, developed in in "Randomized Block Krylov Methods for Stronger and Faster Approximate Singular Value Decomposition". More...
class	RandomizedSVD
	Randomized SVD is a matrix factorization that is based on randomized matrix approximation techniques, developed in in "Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions". More...
class	RegularizedSVD
	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...
class	RegularizedSVDFunction
	The data is stored in a matrix of type MatType, so that this class can be used with both dense and sparse matrix types. More...
class	SVDPlusPlus
	SVD++ is a matrix decomposition tenique used in collaborative filtering. More...
class	SVDPlusPlusFunction
	This class contains methods which are used to calculate the cost of SVD++'s objective function, to calculate gradient of parameters with respect to the objective function, etc. More...