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

This MATLAB implementation provides a complete Probabilistic Latent Semantic Analysis (pLSA) algorithm for text analysis, including comprehensive test datasets and detailed explanations of the underlying mathematical principles. pLSA represents a classical probabilistic model that performs latent semantic analysis on text data to reveal semantic relationships between documents through topic modeling techniques. The core algorithm implements the Expectation-Maximization (EM) procedure, where the E-step calculates posterior probabilities of latent topics given observed words and documents, while the M-step updates model parameters to maximize the likelihood function. Key functions include term-document matrix construction, probability initialization, and iterative EM optimization. Beyond text analysis, this implementation can be effectively adapted for image analysis applications. When applied to visual data, pLSA performs latent semantic analysis on image features (such as SIFT or bag-of-visual-words representations) to identify semantic similarities and contextual relationships between images. The algorithm achieves this by modeling images as documents and visual features as words, enabling semantic clustering and content-based retrieval. In practical applications, this robust algorithm has been widely adopted across various domains, providing powerful tools for text mining, document classification, image categorization, and multimedia content analysis tasks. The MATLAB code includes configurable parameters for topic numbers, convergence thresholds, and initialization methods to accommodate different analysis requirements.