2012
NIPS
NeurIPS 2012
A nonparametric variable clustering model
Abstract
Factor analysis models effectively summarise the covariance structure of high dimensional data, but the solutions are typically hard to interpret. This motivates attempting to find a disjoint partition, i.e. a clustering, of observed variables so that variables in a cluster are highly correlated. We introduce a Bayesian non-parametric approach to this problem, and demonstrate advantages over heuristic methods proposed to date.
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Interdisciplinary Bridge
— Artificial Intelligence and Machine Learning
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Keyword Pioneer
— variable clustering
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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, Speech & Audio
Authors
Topics
Artificial Intelligence > Bayesian & Probabilistic > Bayesian Learning
Artificial Intelligence > Bayesian & Probabilistic > Probabilistic Modeling
Machine Learning > Core Methods > Clustering
Machine Learning > Learning Types > Unsupervised Learning
Machine Learning > Bayesian & Probabilistic > Bayesian Learning
Machine Learning > Core Methods > Dimensionality Reduction
Machine Learning > Bayesian & Probabilistic > Bayesian Inference