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Feature Multi-Selection among Subjective Features
Authors: Sivan Sabato and Adam Kalai
Conference: Proceedings of the 30th International Conference on Machine Learning (ICML-13)
Abstract: When dealing with subjective, noisy, or otherwise nebulous features, the ``wisdom of crowds'' suggests that one may benefit from multiple judgments of the same feature on the same object. We give theoretically-motivated ""feature multi-selection"" algorithms that choose, among a large set of candidate features, not only which features to judge but how many times to judge each one. We demonstrate the effectiveness of this approach for linear regression on a crowdsourced learning task of predicting people's height and weight from photos, using features such as ""gender"" and ""estimated weight"" as well as culturally fraught ones such as ""attractive"".
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