Latent Dirichlet allocation. In natural language processing, latent Dirichlet allocation (LDA) is a generative model that allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar.
For example, if observations are words collected into documents, it posits that each document is a mixture of a small number of topics and that each word's creation is attributable to one of the document's topics. LDA is an example of a topic model and was first presented as a graphical model for topic discovery by David Blei, Andrew Ng, and Michael Jordan in 2003.[1] Topics in LDA[edit] Semisupervised Learning Approaches tom mitchell. Home Page of Thorsten Joachims. · International Conference on Machine Learning (ICML), Program Chair (with Johannes Fuernkranz), 2010. · Journal of Machine Learning Research (JMLR) (action editor, 2004 - 2009). · Machine Learning Journal (MLJ) (action editor). · Journal of Artificial Intelligence Research (JAIR) (advisory board member).
