Clustering from Multiple Uncertain Experts

Yale Chang, Junxiang Chen, Michael Cho, Peter Castaldi, Ed Silverman, Jennifer Dy
; Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, PMLR 54:28-36, 2017.

Abstract

Utilizing expert input often improves clustering performance. However in a knowledge discovery problem, ground truth is unknown even to an expert. Thus, instead of one expert, we solicit the opinion from multiple experts. The key question motivating this work is: which experts should be assigned higher weights when there is disagreement on whether to put a pair of samples in the same group? To model the uncertainty in constraints from different experts, we build a probabilistic model for pairwise constraints through jointly modeling each expert’s accuracy and the mapping from features to latent cluster assignments. After learning our probabilistic discriminative clustering model and accuracies of different experts, 1) samples that were not annotated by any expert can be clustered using the discriminative clustering model; and 2) experts with higher accuracies are automatically assigned higher weights in determining the latent cluster assignments. Experimental results on UCI benchmark datasets and a real-world disease subtyping dataset demonstrate that our proposed approach outperforms competing alternatives, including semi-crowdsourced clustering, semi-supervised clustering with constraints from majority voting, and consensus clustering.

Cite this Paper


BibTeX
@InProceedings{pmlr-v54-chang17a, title = {{Clustering from Multiple Uncertain Experts}}, author = {Yale Chang and Junxiang Chen and Michael Cho and Peter Castaldi and Ed Silverman and Jennifer Dy}, booktitle = {Proceedings of the 20th International Conference on Artificial Intelligence and Statistics}, pages = {28--36}, year = {2017}, editor = {Aarti Singh and Jerry Zhu}, volume = {54}, series = {Proceedings of Machine Learning Research}, address = {Fort Lauderdale, FL, USA}, month = {20--22 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v54/chang17a/chang17a.pdf}, url = {http://proceedings.mlr.press/v54/chang17a.html}, abstract = {Utilizing expert input often improves clustering performance. However in a knowledge discovery problem, ground truth is unknown even to an expert. Thus, instead of one expert, we solicit the opinion from multiple experts. The key question motivating this work is: which experts should be assigned higher weights when there is disagreement on whether to put a pair of samples in the same group? To model the uncertainty in constraints from different experts, we build a probabilistic model for pairwise constraints through jointly modeling each expert’s accuracy and the mapping from features to latent cluster assignments. After learning our probabilistic discriminative clustering model and accuracies of different experts, 1) samples that were not annotated by any expert can be clustered using the discriminative clustering model; and 2) experts with higher accuracies are automatically assigned higher weights in determining the latent cluster assignments. Experimental results on UCI benchmark datasets and a real-world disease subtyping dataset demonstrate that our proposed approach outperforms competing alternatives, including semi-crowdsourced clustering, semi-supervised clustering with constraints from majority voting, and consensus clustering.} }
Endnote
%0 Conference Paper %T Clustering from Multiple Uncertain Experts %A Yale Chang %A Junxiang Chen %A Michael Cho %A Peter Castaldi %A Ed Silverman %A Jennifer Dy %B Proceedings of the 20th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2017 %E Aarti Singh %E Jerry Zhu %F pmlr-v54-chang17a %I PMLR %J Proceedings of Machine Learning Research %P 28--36 %U http://proceedings.mlr.press %V 54 %W PMLR %X Utilizing expert input often improves clustering performance. However in a knowledge discovery problem, ground truth is unknown even to an expert. Thus, instead of one expert, we solicit the opinion from multiple experts. The key question motivating this work is: which experts should be assigned higher weights when there is disagreement on whether to put a pair of samples in the same group? To model the uncertainty in constraints from different experts, we build a probabilistic model for pairwise constraints through jointly modeling each expert’s accuracy and the mapping from features to latent cluster assignments. After learning our probabilistic discriminative clustering model and accuracies of different experts, 1) samples that were not annotated by any expert can be clustered using the discriminative clustering model; and 2) experts with higher accuracies are automatically assigned higher weights in determining the latent cluster assignments. Experimental results on UCI benchmark datasets and a real-world disease subtyping dataset demonstrate that our proposed approach outperforms competing alternatives, including semi-crowdsourced clustering, semi-supervised clustering with constraints from majority voting, and consensus clustering.
APA
Chang, Y., Chen, J., Cho, M., Castaldi, P., Silverman, E. & Dy, J.. (2017). Clustering from Multiple Uncertain Experts. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, in PMLR 54:28-36

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