Dimensionality reduction is a technique used to transform data from a high-dimensional space into a lower-dimensional space while retaining as much meaningful information as possible. This process is essential in various fields, including signal processing, speech recognition, neuroinformatics, and bioinformatics, where datasets often contain a vast number of variables or features.

The primary goal of dimensionality reduction is to simplify the dataset, making it easier to analyze and visualize. High-dimensional data can lead to issues such as sparsity and computational intractability, often referred to as the “curse of dimensionality.” By reducing the number of dimensions, we can mitigate these challenges and improve the performance of machine learning algorithms.

Common techniques for dimensionality reduction include Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE). PCA focuses on identifying the principal components that capture the most variance in the data, while t-SNE is particularly effective for visualizing high-dimensional data by preserving local structures.