lmnn | R Documentation |

An implementation of Large Margin Nearest Neighbors (LMNN), a distance learning technique. Given a labeled dataset, this learns a transformation of the data that improves k-nearest-neighbor performance; this can be useful as a preprocessing step.

lmnn( input, batch_size = NA, center = FALSE, distance = NA, k = NA, labels = NA, linear_scan = FALSE, max_iterations = NA, normalize = FALSE, optimizer = NA, passes = NA, print_accuracy = FALSE, range = NA, rank = NA, regularization = NA, seed = NA, step_size = NA, tolerance = NA, verbose = FALSE )

`input` |
Input dataset to run LMNN on (numeric matrix). |

`batch_size` |
Batch size for mini-batch SGD. Default value "50" (integer). |

`center` |
Perform mean-centering on the dataset. It is useful when the centroid of the data is far from the origin. Default value "FALSE" (logical). |

`distance` |
Initial distance matrix to be used as starting poin (numeric matrix). |

`k` |
Number of target neighbors to use for each datapoint. Default value "1" (integer). |

`labels` |
Labels for input dataset (integer row). |

`linear_scan` |
Don't shuffle the order in which data points are visited for SGD or mini-batch SGD. Default value "FALSE" (logical). |

`max_iterations` |
Maximum number of iterations for L-BFGS (0 indicates no limit). Default value "100000" (integer). |

`normalize` |
Use a normalized starting point for optimization. Itis useful for when points are far apart, or when SGD is returning NaN. Default value "FALSE" (logical). |

`optimizer` |
Optimizer to use; 'amsgrad', 'bbsgd', 'sgd', or 'lbfgs'. Default value "amsgrad" (character). |

`passes` |
Maximum number of full passes over dataset for AMSGrad, BB_SGD and SGD. Default value "50" (integer). |

`print_accuracy` |
Print accuracies on initial and transformed datase. Default value "FALSE" (logical). |

`range` |
Number of iterations after which impostors needs to be recalculate. Default value "1" (integer). |

`rank` |
Rank of distance matrix to be optimized.. Default value "0" (integer). |

`regularization` |
Regularization for LMNN objective function. Default value "0.5" (numeric). |

`seed` |
Random seed. If 0, 'std::time(NULL)' is used. Default value "0" (integer). |

`step_size` |
Step size for AMSGrad, BB_SGD and SGD (alpha). Default value "0.01" (numeric). |

`tolerance` |
Maximum tolerance for termination of AMSGrad, BB_SGD, SGD or L-BFGS. Default value "1e-07" (numeric). |

`verbose` |
Display informational messages and the full list of parameters and timers at the end of execution. Default value "FALSE" (logical). |

This program implements Large Margin Nearest Neighbors, a distance learning technique. The method seeks to improve k-nearest-neighbor classification on a dataset. The method employes the strategy of reducing distance between similar labeled data points (a.k.a target neighbors) and increasing distance between differently labeled points (a.k.a impostors) using standard optimization techniques over the gradient of the distance between data points.

To work, this algorithm needs labeled data. It can be given as the last row of the input dataset (specified with "input"), or alternatively as a separate matrix (specified with "labels"). Additionally, a starting point for optimization (specified with "distance"can be given, having (r x d) dimensionality. Here r should satisfy 1 <= r <= d, Consequently a Low-Rank matrix will be optimized. Alternatively, Low-Rank distance can be learned by specifying the "rank"parameter (A Low-Rank matrix with uniformly distributed values will be used as initial learning point).

The program also requires number of targets neighbors to work with ( specified with "k"), A regularization parameter can also be passed, It acts as a trade of between the pulling and pushing terms (specified with "regularization"), In addition, this implementation of LMNN includes a parameter to decide the interval after which impostors must be re-calculated (specified with "range").

Output can either be the learned distance matrix (specified with "output"), or the transformed dataset (specified with "transformed_data"), or both. Additionally mean-centered dataset (specified with "centered_data") can be accessed given mean-centering (specified with "center") is performed on the dataset. Accuracy on initial dataset and final transformed dataset can be printed by specifying the "print_accuracy"parameter.

This implementation of LMNN uses AdaGrad, BigBatch_SGD, stochastic gradient descent, mini-batch stochastic gradient descent, or the L_BFGS optimizer.

AdaGrad, specified by the value 'adagrad' for the parameter "optimizer", uses maximum of past squared gradients. It primarily on six parameters: the step size (specified with "step_size"), the batch size (specified with "batch_size"), the maximum number of passes (specified with "passes"). Inaddition, a normalized starting point can be used by specifying the "normalize" parameter.

BigBatch_SGD, specified by the value 'bbsgd' for the parameter "optimizer", depends primarily on four parameters: the step size (specified with "step_size"), the batch size (specified with "batch_size"), the maximum number of passes (specified with "passes"). In addition, a normalized starting point can be used by specifying the "normalize" parameter.

Stochastic gradient descent, specified by the value 'sgd' for the parameter "optimizer", depends primarily on three parameters: the step size (specified with "step_size"), the batch size (specified with "batch_size"), and the maximum number of passes (specified with "passes"). In addition, a normalized starting point can be used by specifying the "normalize" parameter. Furthermore, mean-centering can be performed on the dataset by specifying the "center"parameter.

The L-BFGS optimizer, specified by the value 'lbfgs' for the parameter "optimizer", uses a back-tracking line search algorithm to minimize a function. The following parameters are used by L-BFGS: "max_iterations", "tolerance"(the optimization is terminated when the gradient norm is below this value). For more details on the L-BFGS optimizer, consult either the mlpack L-BFGS documentation (in lbfgs.hpp) or the vast set of published literature on L-BFGS. In addition, a normalized starting point can be used by specifying the "normalize" parameter.

By default, the AMSGrad optimizer is used.

A list with several components:

`centered_data` |
Output matrix for mean-centered dataset (numeric matrix). |

`output` |
Output matrix for learned distance matrix (numeric matrix). |

`transformed_data` |
Output matrix for transformed dataset (numeric matrix). |

mlpack developers

# Example - Let's say we want to learn distance on iris dataset with number # of targets as 3 using BigBatch_SGD optimizer. A simple call for the same # will look like: ## Not run: output <- lmnn(input=iris, labels=iris_labels, k=3, optimizer="bbsgd") output <- output$output ## End(Not run) # An another program call making use of range & regularization parameter with # dataset having labels as last column can be made as: ## Not run: output <- lmnn(input=letter_recognition, k=5, range=10, regularization=0.4) output <- output$output ## End(Not run)

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