Hierarchical dirichlet process results

I am thinking about using hierarchical dirichlet process to model a patent dataset. I've seen that HDP uses a base distribution and assumes that every topic comes from that base distribution.

The problem is: first I'm wondering what are the main results from the HDP procedure (in the case of LDA we obtain two matrices that we can use to construct word clouds and graphs but in this case I'm not sure about the results) and what is the exact procedure?

Topic dirichlet unsupervised-learning topic-model data-cleaning data-mining

Category Data Science

1
answered at

TL;DR Ist just a bayesian varianat that generalises LDA.

See here

Basically in the bayesian Setting you dont Need the params, but you can infer them from the data. Using the same dirichlet generative process logic you can cluster grouped data.

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