\section{mlpack\+:\+:svd Namespace Reference}
\label{namespacemlpack_1_1svd}\index{mlpack\+::svd@{mlpack\+::svd}}
\subsection*{Classes}
\begin{DoxyCompactItemize}
\item 
class \textbf{ Bias\+S\+VD}
\begin{DoxyCompactList}\small\item\em Bias S\+VD is an improvement on Regularized S\+VD which is a matrix factorization techniques. \end{DoxyCompactList}\item 
class \textbf{ Bias\+S\+V\+D\+Function}
\begin{DoxyCompactList}\small\item\em This class contains methods which are used to calculate the cost of \doxyref{Bias\+S\+VD}{p.}{classmlpack_1_1svd_1_1BiasSVD}\textquotesingle{}s objective function, to calculate gradient of parameters with respect to the objective function, etc. \end{DoxyCompactList}\item 
class \textbf{ Q\+U\+I\+C\+\_\+\+S\+VD}
\begin{DoxyCompactList}\small\item\em Q\+U\+I\+C-\/\+S\+VD is a matrix factorization technique, which operates in a subspace such that A\textquotesingle{}s approximation in that subspace has minimum error(A being the data matrix). \end{DoxyCompactList}\item 
class \textbf{ Randomized\+Block\+Krylov\+S\+VD}
\begin{DoxyCompactList}\small\item\em Randomized block krylov S\+VD is a matrix factorization that is based on randomized matrix approximation techniques, developed in in \char`\"{}\+Randomized Block Krylov Methods for Stronger and Faster Approximate
\+Singular Value Decomposition\char`\"{}. \end{DoxyCompactList}\item 
class \textbf{ Randomized\+S\+VD}
\begin{DoxyCompactList}\small\item\em Randomized S\+VD is a matrix factorization that is based on randomized matrix approximation techniques, developed in in \char`\"{}\+Finding structure with randomness\+:
\+Probabilistic algorithms for constructing approximate matrix decompositions\char`\"{}. \end{DoxyCompactList}\item 
class \textbf{ Regularized\+S\+VD}
\begin{DoxyCompactList}\small\item\em Regularized S\+VD 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. \end{DoxyCompactList}\item 
class \textbf{ Regularized\+S\+V\+D\+Function}
\begin{DoxyCompactList}\small\item\em The data is stored in a matrix of type Mat\+Type, so that this class can be used with both dense and sparse matrix types. \end{DoxyCompactList}\item 
class \textbf{ S\+V\+D\+Plus\+Plus}
\begin{DoxyCompactList}\small\item\em S\+V\+D++ is a matrix decomposition tenique used in collaborative filtering. \end{DoxyCompactList}\item 
class \textbf{ S\+V\+D\+Plus\+Plus\+Function}
\begin{DoxyCompactList}\small\item\em This class contains methods which are used to calculate the cost of S\+V\+D++\textquotesingle{}s objective function, to calculate gradient of parameters with respect to the objective function, etc. \end{DoxyCompactList}\end{DoxyCompactItemize}