Probabilistic Models: temporal topic models and more
Topic models such as Latent Dirichlet Allocation (LDA) have been used successfully in many domains for data mining. Originally designed for text documents, these methods find some hidden “topics” considering that each document is a weighted mixture of topics. Each topic expresses itself in a document by generating some specific words with more probability than others.
Topic models have been used with various kinds of data ranging including text, image, video and mixtures of these. When applied to temporal data like videos, topic models needs to be extended to take into account the time dimension. Different approaches have been proposed towards this inclusion of the time information.