主观特征间的多特征选择
Feature Multi-Selection among Subjective Features
摘要：
在处理主观的、嘈杂的或其他模糊的特征时，“群体智慧”表明，人们可以从对同一对象的同一特征的多个判断中受益。我们给理论上的动机“功能多选择”算法，选择，在一个大的候选功能，不仅哪些功能判断，但多少次判断每一个。我们证明了这种方法在众包学习任务中的线性回归的有效性，该任务是从照片中预测人们的身高和体重，使用诸如“性别”和“估计体重”以及诸如“吸引力”等文化上令人担忧的特征。
英文摘要：
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'.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
