2011
NeurIPS
NeurIPS 2011
Crowdclustering
Abstract
Is it possible to crowdsource categorization? Amongst the challenges: (a) each annotator has only a partial view of the data, (b) different annotators may have different clustering criteria and may produce different numbers of categories, (c) the underlying category structure may be hierarchical. We propose a Bayesian model of how annotators may approach clustering and show how one may infer clusters/categories, as well as annotator parameters, using this model. Our experiments, carried out on large collections of images, suggest that Bayesian crowdclustering works well and may be superior to single-expert annotations.
🌉
Interdisciplinary Bridge
- Artificial Intelligence and Data Science & Analytics
🧭
Keyword Pioneer
- partial annotation
🐣
Hot Topic Early Bird
- hierarchical clustering
Authors
Topics
Artificial Intelligence > Bayesian & Probabilistic > Bayesian Learning
Machine Learning > Core Methods > Clustering
Machine Learning > Learning Types > Unsupervised Learning
Machine Learning > Optimization & Theory > Bayesian Inference
Data Science & Analytics > Applications > Clustering
Machine Learning > Bayesian & Probabilistic > Bayesian Learning
Artificial Intelligence > Bayesian & Probabilistic > Bayesian Inference
Keywords
bayesian inference
unsupervised clustering
hierarchical clustering
bayesian model
annotator modeling
image categorization
crowdsourced clustering
crowd clustering
annotator aggregation
partial annotation
hierarchical category
crowdsourcing categorization
cluster inference
crowdsourced categorization
annotator parameter