Random Projection

Random projection is a dimensionality reduction technique that projects high-dimensional data onto a lower-dimensional subspace using random matrices, preserving key properties like distances and similarities with high probability. Current research focuses on applying random projections within various machine learning models, including neural networks (e.g., Random Projection Neural Networks, RandONets), Bayesian optimization (CEPBO), and clustering algorithms (sDBSCAN), to improve efficiency and scalability while maintaining accuracy. This technique is proving valuable across diverse fields, enhancing the performance of algorithms in applications ranging from time series forecasting and graph learning to solving linear programs and accelerating training of large neural networks.