Scalable Machine Learning Models for High-Dimensional Datasets
A systematic analysis of scalable machine learning models for high-dimensional data is presented, outlining structural challenges, reviewing scalable learning paradigms, and proposing a unified methodological framework that integrates feature reduction, model parallelism, and adaptive optimization.