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... | |