Feature Multi-Selection among Subjective Features

Sivan Sabato, Adam Kalai
; Proceedings of the 30th International Conference on Machine Learning, PMLR 28(3):810-818, 2013.

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"".

Cite this Paper


BibTeX
@InProceedings{pmlr-v28-sabato13, title = {Feature Multi-Selection among Subjective Features}, author = {Sivan Sabato and Adam Kalai}, booktitle = {Proceedings of the 30th International Conference on Machine Learning}, pages = {810--818}, year = {2013}, editor = {Sanjoy Dasgupta and David McAllester}, volume = {28}, number = {3}, series = {Proceedings of Machine Learning Research}, address = {Atlanta, Georgia, USA}, month = {17--19 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v28/sabato13.pdf}, url = {http://proceedings.mlr.press/v28/sabato13.html}, 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"".} }
Endnote
%0 Conference Paper %T Feature Multi-Selection among Subjective Features %A Sivan Sabato %A Adam Kalai %B Proceedings of the 30th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2013 %E Sanjoy Dasgupta %E David McAllester %F pmlr-v28-sabato13 %I PMLR %J Proceedings of Machine Learning Research %P 810--818 %U http://proceedings.mlr.press %V 28 %N 3 %W PMLR %X 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"".
RIS
TY - CPAPER TI - Feature Multi-Selection among Subjective Features AU - Sivan Sabato AU - Adam Kalai BT - Proceedings of the 30th International Conference on Machine Learning PY - 2013/02/13 DA - 2013/02/13 ED - Sanjoy Dasgupta ED - David McAllester ID - pmlr-v28-sabato13 PB - PMLR SP - 810 DP - PMLR EP - 818 L1 - http://proceedings.mlr.press/v28/sabato13.pdf UR - http://proceedings.mlr.press/v28/sabato13.html AB - 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"". ER -
APA
Sabato, S. & Kalai, A.. (2013). Feature Multi-Selection among Subjective Features. Proceedings of the 30th International Conference on Machine Learning, in PMLR 28(3):810-818

Related Material