IBP linear-Gaussian model

(1.2 hours to learn)


The linear-Gaussian IBP model is a simple matrix factorization model, where the model assumes the observed data results from linearly combining a subset of K independent real-valued latent factors: X = Z x A + E, where X is the N x D observed data matrix, Z is the N x K binary latent feature matrix, A is the K x D latent real-valued factor matrix, and E is N x D matrix of iid noise. Using an IBP prior allows the number of latent features, K, to be learned from the data. This model is commonly used for developing new IBP inference techniques.


This concept has the prerequisites:

Core resources (read/watch one of the following)


The Indian Buffet Process: Scalable Inference and Extensions
Location: appendix A.1
Author: Finale Doshi-Velez
The Indian Buffet Process: an Introduction and Review
Location: section 5.1
Authors: Tom Griffiths,Zoubin Ghahramani

See also

-No Additional Notes-