In this chapter, we discussed a more advanced form of grouping documents, which is more flexible than simple clustering as we allow each document to be present in more than one group. We explored the basic LDA model using a new package, gensim, but were able to integrate it easily into the standard Python scientific ecosystem.

Topic modeling was first developed and is easier to understand in the case of text, but in Chapter 10, Computer Vision – Pattern Recognition, we will see how some of these techniques may be applied to images as well. Topic models are very important in most of modern computer vision research. In fact, unlike the previous chapters, this chapter was very close to the cutting edge of research in machine learning algorithms. The original LDA algorithm was published in a scientific journal in 2003, but the method that gensim uses to be able to handle Wikipedia was only developed in 2010, and the HDP algorithm is from 2011. The research continues and you can find many variations and models with wonderful names such as the Indian buffet process (not to be confused with the Chinese restaurant process, which is a different model), or Pachinko allocation (Pachinko being a type of Japanese game, a cross between a slot-machine and pinball). Currently, they are still in the realm of research. In a few years, though, they might make the jump into the real world.

We have now gone over some of the major machine learning models such as classification, clustering, and topic modeling. In the next chapter, we go back to classification, but this time we will be exploring advanced algorithms and approaches.