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# Large Margin Nearest Neighbors
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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.
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# Available Predicates
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* [lmnn/20](/PrologMethods/Transformation/lmnn#lmnn20)
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---
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[links/resources](/PrologMethods/Transformation/lmnn#connected-linksresources)
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## **_lmnn/20_**
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Is a single predicate that initiates the lmnn model with all the given params and then performs Large Margin Nearest Neighbors metric learning on the reference data.
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```prolog
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%% part of the predicate definition
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lmnn( +string,
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+pointer(float_array), +integer, +integer,
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+pointer(float_array), +integer,
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+integer,
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+float32, +float32, +integer, +integer, +float32,
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+integer, +integer,
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+integer, +integer, +integer,
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-pointer(float_array), -integer, -integer)
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```
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### Parameters
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| Name | Type | Description | Default |
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|------|------|-------------|---------|
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| optimizer | +string | Optimizer to use; "amsgrad", "bbsgd", "sgd", or "lbfgs". | amsgrad |
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| data | +matrix | Input dataset to run LMNN on. | - |
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| labels | +vec | Labels for input dataset. | - |
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| k | +integer | Number of target neighbors to use for each datapoint. | 1 |
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| regularization | +float | Regularization for LMNN objective function. | 0.5 |
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| stepSize | +float | Step size for AMSGrad, BB_SGD and SGD (alpha). | 0.01 |
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| passes | +integer | Maximum number of full passes over dataset for AMSGrad, BB_SGD and SGD. | 50 |
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| maxIterations | +integer | Maximum number of iterations for L-BFGS (0 indicates no limit). | 100000 |
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| tolerance | +integer | Maximum tolerance for termination of AMSGrad, BB_SGD, SGD or L-BFGS. | 1e-7 |
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| center | +integer(bool) | Perform mean-centering on the dataset. It is useful when the centroid of the data is far from the origin. | (0)false |
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| shuffle | +integer(bool) | | |
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| batchSize | +integer | Batch size for mini-batch SGD. | 50 |
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| range | +integer | Number of iterations after which impostors needs to be recalculated. | 1 |
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| rank | +integer | Rank of distance matrix to be optimized. | 0 |
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| distance | -matrix | Output matrix for learned distance matrix. | - |
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---
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# Connected Links/Resources
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If you want a more detailed explanation, then go to the python documentation. There is most of the time a good explanation on how the methods work and what the parameters do.
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* [MLpack::lmnn_C++\_documentation](https://www.mlpack.org/doc/stable/doxygen/classmlpack_1_1lmnn_1_1LMNN.html)
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* [MLpack::lmnn_Python_documentation](https://www.mlpack.org/doc/stable/python_documentation.html#lmnn)
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added some of the links from the python documentation
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* nca
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* [Large margin nearest neighbor on Wikipedia](https://en.wikipedia.org/wiki/Large_margin_nearest_neighbor)
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* [Distance metric learning for large margin nearest neighbor classification (pdf)](http://papers.nips.cc/paper/2795-distance-metric-learning-for-large-margin-nearest-neighbor-classification.pdf) |
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\ No newline at end of file |
