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Large margin nearest neighbor classification is a statistical machine learning algorithm for metric learning. It learns a pseudometric designed for k-nearest neighbor classification. The algorithm is based on semidefinite programming, a sub-class of convex optimization.
The goal of supervised learning is to learn a decision rule that can categorize data instances into pre-defined classes. The k-nearest neighbor rule assumes a training data set of labeled instances. It classifies a new data instance with the class obtained from the majority vote of the k closest training instances. Closeness is measured with a pre-defined metric. Large margin nearest neighbors is an algorithm that learns this global metric in a supervised fashion to improve the classification accuracy of the k-nearest neighbor rule.