MATLAB Implementation of LDA (Latent Dirichlet Allocation) Algorithm

LDA (Latent Dirichlet Allocation) is a widely-used topic modeling algorithm extensively applied in text mining, information retrieval, and natural language processing. Implementing LDA in MATLAB enables users to extract thematic structures from large document collections for dimensionality reduction or classification tasks. The core concept of LDA assumes each document is a mixture of multiple topics, while each topic is represented as a probability distribution over words. Through iterative optimization, LDA learns document-topic distributions and topic-word distributions, ultimately revealing latent semantic structures in document collections. MATLAB implementation of LDA typically involves the following steps: Data preprocessing: Convert text data into bag-of-words models or TF-IDF matrices, remove stop words, and perform necessary normalization using functions like bagOfWords and tfidf. Parameter initialization: Determine the number of topics (K), Dirichlet hyperparameters (α and β), and randomly initialize document-topic and topic-word distributions using rand or randn functions. Gibbs sampling or variational inference: Employ iterative optimization methods like Gibbs sampling (implemented through loop structures with categorical distributions) to adjust topic assignments until convergence or maximum iterations are reached. Result analysis: Extract high-probability words for each topic and visualize topic-document relationships using MATLAB's visualization tools like wordcloud or heatmap for downstream tasks such as classification and clustering. MATLAB's strength lies in its powerful matrix operations capability, making it suitable for handling high-dimensional sparse text data through efficient sparse matrix operations. Additionally, users can leverage MATLAB's visualization tools like wordcloud and heatmap to intuitively display LDA results. For researchers seeking rapid LDA implementation, MATLAB provides clear code structures and rich statistical toolbox functions like mle and fitcdiscr, enabling efficient model tuning and result analysis. Key functions include text preprocessing utilities from Text Analytics Toolbox and optimization algorithms for Bayesian inference.