For data analysis, Random mapping (RM) is a fast dimensionality reduction method  categorized as feature extraction method. The RM consists in generation of a random matrix that is multiplied by each original vector and result in a reduced vector. When the data vectors are high-dimensional it is computationally infeasible to use data analysis or pattern recognition algorithms which repeatedly compute similarities or distances in the original data space.
Random mapping (RM) is a fast way to reduce the number of dimensions in data, making it easier to analyze and recognize patterns. It works by creating a random matrix that's multiplied by each original vector, resulting in a smaller, more manageable representation. This method is particularly useful when dealing with high-dimensional datasets because traditional algorithms can become computationally intensive for such large spaces.