Implement Metric Learning Algorithms with Applications to Metagenomics
by Fernando José Iglesias García for Shogun Machine Learning Toolbox
Metric learning algorithms constitute an interesting approach in which a transformation of the data is sought in order to maximize classification accuracy. This property together with the use of the rather successful kNN algorithm make these algorithms suitable for real-world problems in fields such as bioinformatics. We aim at implementing the large margin nearest neighbor classifier and expose it in a easy-to-use manner, contributing to the metagenomics research community.