Simultaneous Learning of Trees and Representations for Extreme Classification and Density Estimation
This addresses the challenge of handling very large label sets in classification and density estimation, though it appears incremental by building on prior work that learned tree structures separately.
The paper tackles the problem of multi-class classification with hierarchical predictors by introducing an algorithm that simultaneously learns tree structures and data representations, showing favorable accuracy and running time compared to baselines in text classification and language modeling.
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. We theoret- ically analyze this objective, showing that it gives rise to a boosting style property and a bound on classification error. We next show how to extend the algorithm to conditional density estimation. We empirically validate both variants of the al- gorithm on text classification and language mod- eling, respectively, and show that they compare favorably to common baselines in terms of accu- racy and running time.