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

Yacine Jernite,A. Choromańska,D. Sontag

Published 2016 in International Conference on Machine Learning

ABSTRACT

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 hierarchical predictor. Our approach optimizes an objective function which favors balanced and easily-separable multi-way node partitions. We theoretically 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 algorithm on text classification and language modeling, respectively, and show that they compare favorably to common baselines in terms of accuracy and running time.

PUBLICATION RECORD

  • Publication year

    2016

  • Venue

    International Conference on Machine Learning

  • Publication date

    2016-10-14

  • Fields of study

    Mathematics, Computer Science

  • Identifiers
    arXiv 1610.04658
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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