Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
A unifying review of linear Gaussian models
Neural Computation
Particle filters for mixture models with an unknown number of components
Statistics and Computing
Bayesian hierarchical clustering
ICML '05 Proceedings of the 22nd international conference on Machine learning
A choice model with infinitely many latent features
ICML '06 Proceedings of the 23rd international conference on Machine learning
Accelerated sampling for the Indian Buffet Process
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Learning systems of concepts with an infinite relational model
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Infinite sparse factor analysis and infinite independent components analysis
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Correlated non-parametric latent feature models
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Multi-assignment clustering for boolean data
The Journal of Machine Learning Research
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The Indian buffet process is a stochastic process defining a probability distribution over equivalence classes of sparse binary matrices with a finite number of rows and an unbounded number of columns. This distribution is suitable for use as a prior in probabilistic models that represent objects using a potentially infinite array of features, or that involve bipartite graphs in which the size of at least one class of nodes is unknown. We give a detailed derivation of this distribution, and illustrate its use as a prior in an infinite latent feature model. We then review recent applications of the Indian buffet process in machine learning, discuss its extensions, and summarize its connections to other stochastic processes.