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arXiv · 1109.0820

ShareBoost: Efficient Multiclass Learning with Feature Sharing

Shai Shalev-Shwartz·Yonatan Wexler·Amnon Shashua
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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.

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BibTeXRIS

Shai Shalev-Shwartz, Yonatan Wexler, Amnon Shashua. 2011-09-05. ShareBoost: Efficient Multiclass Learning with Feature Sharing. https://arxiv.org/abs/1109.0820

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