Feature Multi-Selection among Subjective Features

Sivan Sabato, Adam Kalai

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

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 crowd-sourced 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.

Original languageEnglish
Title of host publicationProceedings of the 30th International Conference on Machine Learning (ICML), JMLR Workshop and Conference Proceedings
Pages810-818
Volume28(3)
StatePublished - 2013
Externally publishedYes
Event30th International Conference on Machine Learning, ICML 2013 - Atlanta, GA, United States
Duration: 16 Jun 201321 Jun 2013

Conference

Conference30th International Conference on Machine Learning, ICML 2013
Country/TerritoryUnited States
CityAtlanta, GA
Period16/06/1321/06/13

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