MATLAB Implementation of LDA Algorithm

I have spent considerable time searching for a quality implementation of LDA (Latent Dirichlet Allocation) and finally discovered one that meets my standards. LDA is a probabilistic topic modeling algorithm designed to identify latent topics and key terms within document collections. It finds widespread applications in natural language processing, information retrieval, and machine learning domains. This particular implementation incorporates several advanced techniques to enhance both computational efficiency and model accuracy. The codebase features optimized Gibbs sampling procedures with proper hyperparameter tuning, efficient sparse matrix operations for handling large document-term matrices, and convergence detection mechanisms for training stabilization. The author provides well-documented MATLAB scripts including core functions for model initialization, topic inference, and result visualization, along with detailed comments explaining key algorithmic steps. For researchers and practitioners interested in topic modeling, this implementation offers practical insights into LDA's working mechanism through executable code examples. The package includes demonstration scripts showing how to preprocess text data, train topic models, and interpret results through topic-word distributions and document-topic assignments. I encourage you to explore this implementation as it provides valuable hands-on experience with probabilistic graphical models.