Random Projection

Random Projection is a dimensionality reduction technique used in data science and machine learning to simplify high-dimensional datasets while preserving their essential structure. By projecting data points onto a lower-dimensional space using random matrices, this method maintains the pairwise distances between samples with a controlled level of distortion. It is particularly useful for speeding up computations and reducing memory usage in large datasets. Random Projection is grounded in the Johnson-Lindenstrauss lemma, which guarantees that the distances between points remain approximately preserved, making it an effective tool for various applications, including similarity search and clustering.

6.6. Random Projection

Scikit-learn User Guide

The sklearn.random_projection module implements a simple and computationally efficient way to reduce the dimensionality of the data by trading a controlled amount of accuracy (as additional varianc......

The Johnson-Lindenstrauss bound for embedding with random projections

Scikit-learn Examples

The Johnson-Lindenstrauss bound for embedding with random projections The Johnson-Lindenstrauss lemma states that any high dimensional dataset can be randomly projected into a lower dimensional Euclid...

Similarity Search, Part 6: Random Projections with LSH Forest

Towards Data Science

Understand how to hash data and reflect its similarity by constructing random hyperplanes Similarity search is a problem where given a query the goal is to find the most similar documents to it among...