Information System of Semi-supervised Learning for Analysis of Data Samples With High Dimensions

The study of large datasets to uncover hidden patterns and trends has become increasingly important and valuable in recent years. These large datasets are characterized by wide availability, structural complexity, and significant volume of information. This article proposes a detailed description of a semi-supervised learning information system for analyzing high-dimensional data samples. The system is designed to process large datasets using semi- supervised learning methods for effective analysis and classification. Existing information systems capable of working with high-dimensional data samples, as well as methods for efficient analysis and classification of these data samples, were analyzed for this purpose. The article provides a detailed description of the system architecture, including data processing methods, feature selection, preprocessing modules, and training optimization methods.