Collapsed gibbs sampler for sparse topic models and discrete matrix factorization

A method comprising:

• generating feature representations comprising distributions over a set of features corresponding to objects d=1, . . . , D of a training corpus of D objects;
• and
• inferring a generative topic model defining a set of topics k=1, . . . , K by performing a latent generative topic model allocation using a collapsed Gibbs sampling algorithm with an Indian Buffet Process (IBP) compound prior probability distribution having binary activation variables θεK×D and object-specific topic proportions θd| θd˜Dirichlet(α θd) with weights α that are the same for all the objects d=1, . . . , D,
• wherein the inferring includes iterative sampling of (1) topic allocation variables of the generative topic model allocation and (2) the binary activation variables θ of the IBP compound prior probability distribution;
• and
• wherein the generating and inferring are performed by a digital processor.

An apparatus comprising:

• a digital processor configured to perform a method including:
• generating feature representations comprising distributions over a set of features corresponding to documents d=1, . . . , D of a training corpus of D documents;
• and
• inferring a generative latent Dirichlet allocation (LDA) or probabilistic latent semantic analysis (PLSA) topic model defining a set of topics k=1, . . . , K by performing a latent generative topic model allocation using a collapsed Gibbs sampling algorithm with an Indian Buffet Process (IBP) compound prior probability distribution having binary activation variables θεK×D and document-specific topic proportions θd| θd˜Dirichlet(α θd) with weights α that are the same for all the documents d=1, . . . , D;
• wherein the inferring includes iterative sampling of (1) topic allocation variables of the generative topic model allocation and (2) the binary activation variables θ of the IBP compound prior probability distribution.