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 dimensionality reduction technique categorized as feature extraction method. The RM involves generating a random matrix that is multiplied by each original vector, resulting in a reduced vector. When dealing with high-dimensional data, computationally expensive algorithms for similarity or distance computations become infeasible.