Simultaneous Learning of Trees and Representations for Extreme Classification and Density Estimation

Understand

We consider multi-class classification where the predictor has a hierarchical structure that allows for a very large number of labels both at train and test time.

• The predictive power of such models can heavily depend on the structure of the tree, and although past work showed how to learn the tree structure, it expected that the feature vectors remained static.
• We provide a novel algorithm to simultaneously perform representation learning for the input data and learning of the hierarchi- cal predictor.
• Our approach optimizes an objec- tive function which favors balanced and easily- separable multi-way node partitions.