Probabilistic Latent Semantic Analysis (PLSA) is a statistical method for analyzing two-mode and co-occurrence data, with applications in information retrieval, natural language processing, and machine learning. Unlike standard Latent Semantic Analysis (LSA), which uses Singular Value Decomposition (SVD) of co-occurrence tables, PLSA is based on a mixture decomposition derived from a latent class model, providing a more principled statistical foundation. PLSA avoids overfitting by using Tempered Expectation Maximization (TEM), a generalization of maximum likelihood estimation that improves model generalization. PLSA outperforms LSA in various experiments, particularly in tasks like perplexity minimization and information retrieval. The method models word occurrences as a mixture of latent classes, allowing for the identification of semantic relationships between words and documents. PLSA's probabilistic approach enables better interpretation of latent dimensions and provides a solid statistical foundation for model selection and complexity control. Experimental results show that PLSA achieves significant performance gains over LSA, demonstrating its effectiveness in text learning and information retrieval. The method is considered a promising unsupervised learning technique with wide-ranging applications.Probabilistic Latent Semantic Analysis (PLSA) is a statistical method for analyzing two-mode and co-occurrence data, with applications in information retrieval, natural language processing, and machine learning. Unlike standard Latent Semantic Analysis (LSA), which uses Singular Value Decomposition (SVD) of co-occurrence tables, PLSA is based on a mixture decomposition derived from a latent class model, providing a more principled statistical foundation. PLSA avoids overfitting by using Tempered Expectation Maximization (TEM), a generalization of maximum likelihood estimation that improves model generalization. PLSA outperforms LSA in various experiments, particularly in tasks like perplexity minimization and information retrieval. The method models word occurrences as a mixture of latent classes, allowing for the identification of semantic relationships between words and documents. PLSA's probabilistic approach enables better interpretation of latent dimensions and provides a solid statistical foundation for model selection and complexity control. Experimental results show that PLSA achieves significant performance gains over LSA, demonstrating its effectiveness in text learning and information retrieval. The method is considered a promising unsupervised learning technique with wide-ranging applications.