2011
NIPS
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 Machine Learning
🧭
Keyword Pioneer
— crowdsourced clustering
🐝
Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
📈
Trend Setter
— Unsupervised Learning
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Hot Topic Early Bird
— hierarchical clustering
Authors
Topics
Artificial Intelligence > Bayesian & Probabilistic > Bayesian Learning
Machine Learning > Core Methods > Clustering
Data Science & Analytics > Applications > Clustering
Machine Learning > Bayesian & Probabilistic > Bayesian Learning
Machine Learning > Bayesian & Probabilistic > Bayesian Inference
Machine Learning > Learning Paradigms > Unsupervised Learning