Date of Original Version



Conference Proceeding

Journal Title

Journal of Machine Learning Research : Workshop and Conference Proceedings



First Page


Last Page


Rights Management

Copyright 2014 by the author(s).

Abstract or Description

In many high-dimensional learning problems, only some parts of an observation are important to the prediction task; for example, the cues to correctly categorizing a document may lie in a handful of its sentences. We introduce a learning algorithm that exploits this intuition by encoding it in a regularizer. Specifically, we apply the sparse overlapping group lasso with one group for every bundle of features occurring together in a training-data sentence, leading to thousands to millions of overlapping groups. We show how to efficiently solve the resulting optimization challenge using the alternating directions method of multipliers. We find that the resulting method significantly outperforms competitive baselines (standard ridge, lasso, and elastic net regularizers) on a suite of real-world text categorization problems.



Published In

Journal of Machine Learning Research : Workshop and Conference Proceedings, 32, 656-664.