2012/05/09 by Finale Doshi-Velez, Finale Doshi‐Velez, Zoubin Ghahramani +2 · 30 citations
Computer Science · Mathematics · #Artificial intelligence #Bayesian Methods and Mixture Models #Computer science #Data mining #Data set #Econometrics #FOS: Computer and information sciences #Feature (linguistics) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine learning #Mathematics #Nonparametric statistics #Parametric model #Parametric statistics #Pattern recognition (psychology) #Process (computing) #Set (abstract data type) #Statistical Methods and Inference #Statistics #Time Series Analysis and Forecasting #Uncorrelated #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1205.2650
published in arXiv (Cornell University), 143-150 (Cornell University) · Appears in Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (UAI2009)
arxiv created 2012/05/09 · openalex publication_date 2012/05/09 · arxiv updated 2012/05/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We are often interested in explaining data through a set of hidden factors or features. When the number of hidden features is unknown, the Indian Buffet Process (IBP) is a nonparametric latent feature model that does not bound the number of active features in dataset. However, the IBP assumes that all latent features are uncorrelated, making it inadequate for many realworld problems. We introduce a framework for correlated nonparametric feature models, generalising the IBP. We use this framework to generate several specific models and demonstrate applications on realworld datasets.