GibbsLDASharp

Project Description
GibbsLDASharp is a C# implementation of Latent Dirichlet Allocation (LDA) using Gibbs Sampling technique for parameter estimation and inference.



GibbsLDASharp is a C# implementation of Latent Dirichlet Allocation (LDA) using Gibbs Sampling technique for parameter estimation and inference. It is very fast and is designed to analyze hidden/latent topic structures of large-scale datasets including large collections of text/Web documents. LDA was first introduced by David Blei et al Blei03.

- Information retrieval and search (analyzing semantic/latent topic/concept structures of large text collection for a more intelligent information search).
- Document classification/clustering, document summarization, and text/web mining community in general.
- Content-based image clustering, object recognition, and other applications of computer vision in general.
- Other potential applications in biological data.

This Project is migrated from GibbsLDA++, a C++ implementation of Latent Dirichlet Allocation (LDA) using Gibbs Sampling technique for parameter estimation and inference.

GibbsLDA++'s homepage is available here: GibbsLDA++.