Feature Multi-Selection among Subjective Features

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 theoreticallymotivated 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.