Sparse Compositional Metric Learning is a software to learn metrics in the form of sparse combinations of simple basis elements (obtained for instance from Linear Discriminant Analysis), which allows it to scale well with the data dimensionality. It can be used to learn a single global metric or multiple local metrics that vary smoothly across the feature space. It also supports the multi-task setting, where a metric is learned for each task in a coupled fashion. All formulations are solved in a scalable way using stochastic optimization techniques.
Associated paper: Y. Shi, A. Bellet and F. Sha. Sparse Compositional Metric Learning. AAAI Conference on Artificial Intelligence (AAAI), 2014, 2078-2084.
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Initial Announcement on mloss.org.
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