Time-Varying Topic Models using Dependent Dirichlet Processes

We lay the ground for extending Dirichlet Processes based clustering and factor models to explicitly include variability as a function of time (or other known covariates) by integrating a Dependent Dirichlet Processes into existing hierarchical topic models. Time-Varying Topic Models using Dependent Dirichlet Processes Nathan Srebro Sam Roweis Dept. of Computer Science, University of Toronto, Canada {nati,roweis}@cs.toronto.edu Abstract We lay the ground for extending Dirichlet Processes based clustering and factor models to explicitly include variability as a function of time (or other known covariates) by integrating a Dependent Dirichlet Processes into existing hierarchical topic models.We lay the ground for extending Dirichlet Processes based clustering and factor models to explicitly include variability as a function of time (or other known covariates) by integrating a Dependent Dirichlet Processes into existing hierarchical topic models.