Latent Space
Machine Learning
A generative statistical model that allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar.
Detailed Explanation
Latent Dirichlet Allocation (LDA) is a probabilistic model used in machine learning for topic modeling. It assumes documents are mixtures of latent topics, which are distributions over words. LDA uncovers these hidden thematic structures, helping to explain patterns within large text datasets by identifying groups of words that commonly co-occur, thereby revealing underlying themes.
Use Cases
- •Analyze large document collections to automatically identify and organize major themes or topics within the text data.
Related Terms
Other terms in the Machine Learning category