MATLAB Implementation of pLSA (Probabilistic Latent Semantic Analysis) for Text Analysis and Classification

This MATLAB implementation provides a comprehensive probabilistic latent semantic analysis (pLSA) algorithm for text analysis and classification tasks. The package includes test datasets and detailed explanations of the underlying algorithm principles, with potential applications extending to image analysis domains. The updated version introduces a demonstration script (demo.m) that provides visualization capabilities, making the pLSA algorithm more accessible and understandable for users. The implementation includes proper matrix operations for term-document co-occurrence statistics and employs the Expectation-Maximization (EM) algorithm for parameter estimation, ensuring efficient latent topic discovery. Additionally, the new version features enhanced documentation with step-by-step usage instructions, helping users master the practical application of the pLSA algorithm. The package now includes expanded sample datasets demonstrating the algorithm's performance across various scenarios. The theoretical section has been significantly improved with more comprehensive background information and detailed mathematical explanations of the probability model, including the derivation of the EM algorithm update rules for P(z|d) and P(w|z) distributions. Performance and stability improvements have been implemented to handle large-scale datasets and complex problems more effectively. The code optimization includes efficient sparse matrix handling for term-document matrices, convergence criteria implementation for the EM algorithm, and memory management techniques for processing high-dimensional data. These enhancements result in improved computational efficiency and accuracy for practical applications. The updated pLSA implementation represents a comprehensive enhancement over previous versions, offering improved user experience and expanded functionality. Whether applied to text analysis, classification tasks, or image analysis scenarios, this pLSA algorithm serves as a powerful tool for probabilistic topic modeling and latent semantic discovery. The implementation properly handles the probabilistic relationships between documents, latent topics, and words through well-structured MATLAB code that follows mathematical principles of the pLSA framework.