ShareBoost: Efficient Multiclass Learning with Feature Sharing
Abstract
Multiclass prediction is the problem of classifying an object into a relevant target class. We consider the problem of learning a multiclass predictor that uses only few features, and in particular, the number of used features should increase sub-linearly with the number of possible classes. This implies that features should be shared by several classes. We describe and analyze the ShareBoost algorithm for learning a multiclass predictor that uses few shared features. We prove that ShareBoost efficiently finds a predictor that uses few shared features (if such a predictor exists) and that it has a small generalization error. We also describe how to use ShareBoost for learning a non-linear predictor that has a fast evaluation time. In a series of experiments with natural data sets we demonstrate the benefits of ShareBoost and evaluate its success relatively to other state-of-the-art approaches.
Cite
@article{arxiv.1109.0820,
title = {ShareBoost: Efficient Multiclass Learning with Feature Sharing},
author = {Shai Shalev-Shwartz and Yonatan Wexler and Amnon Shashua},
journal= {arXiv preprint arXiv:1109.0820},
year = {2011}
}