Collapsed gibbs sampler for sparse topic models and discrete matrix factorization
US8510257B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Oct 19, 2010 |
| Grant date | Aug 13, 2013 |
| Priority date | — |
| Expiry date | Nov 16, 2031 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N7/01
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
In an inference system for organizing a corpus of objects, feature representations are generated comprising distributions over a set of features corresponding to the objects. A topic model defining a set of topics is inferred by performing latent Dirichlet allocation (LDA) with an Indian Buffet Process (IBP) compound Dirichlet prior probability distribution. The inference is performed using a collapsed Gibbs sampling algorithm by iteratively sampling (1) topic allocation variables of the LDA and (2) binary activation variables of the IBP compound Dirichlet prior. In some embodiments the inference is configured such that each inferred topic model is a clean topic model with topics defined as distributions over sub-sets of the set of features selected by the prior. In some embodiments the inference is configured such that the inferred topic model associates a focused sub-set of the set of topics to each object of the training corpus.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.