  Extreme classification seeks to assign each data point, the most relevant
labels from a universe of a million or more labels. This task is faced with the
dual challenge of high precision and scalability, with millisecond level
prediction times being a benchmark. We propose DEFRAG, an adaptive feature
agglomeration technique to accelerate extreme classification algorithms.
Despite past works on feature clustering and selection, DEFRAG distinguishes
itself in being able to scale to millions of features, and is especially
beneficial when feature sets are sparse, which is typical of recommendation and
multi-label datasets. The method comes with provable performance guarantees and
performs efficient task-driven agglomeration to reduce feature dimensionalities
by an order of magnitude or more. Experiments show that DEFRAG can not only
reduce training and prediction times of several leading extreme classification
algorithms by as much as 40%, but also be used for feature reconstruction to
address the problem of missing features, as well as offer superior coverage on
rare labels.

Machine Learning;Artificial Intelligence;Machine Learning