Sparse Machine Learning Models for High-Dimensional Data Analysis
TL;DR
Experimental findings show that sparse models provide better robustness, faster convergence, lower memory usage, and reduced training complexity than traditional dense models.
Abstract
High-dimensional data analysis has become increasingly important in machine learning, computational intelligence, bioinformatics, finance, and image processing due to the rapid growth of large-scale datasets generated by IoT, cloud computing, and digital technologies. These datasets often contain thousands of features with limited samples, creating challenges such as overfitting, high computational complexity, redundancy, and the curse of dimensionality. Sparse machine learning models address these issues by using sparsity constraints and regularization techniques to select the most relevant features while eliminating irrelevant data. Popular methods include LASSO, Elastic Net, Sparse PCA, Sparse Autoencoders, and Sparse Support Vector Machines. These approaches improve interpretability, scalability, computational efficiency, and predictive performance. Sparse learning is widely applied in genomics, cybersecurity, medical diagnosis, recommender systems, and industrial automation. This study reviews sparse optimization, feature selection, and sparse representation learning techniques for high-dimensional data analysis. Experimental findings show that sparse models provide better robustness, faster convergence, lower memory usage, and reduced training complexity than traditional dense models. Future research focuses on integrating sparse learning with deep learning, federated learning, reinforcement learning, and explainable AI systems.