Introduction to Dimensionality Reduction

Dimensionality reduction is a technique used to reduce the number of features in a dataset while retaining key information. It addresses the curse of dimensionality by transforming high-dimensional data into a lower-dimensional space, preserving the essence of the original data. There are two main approaches to dimensionality reduction: feature selection and feature extraction. Feature selection involves choosing a subset of the most relevant features, while feature extraction creates new features by combining or transforming original ones. Popular methods include PCA, LDA, and t-SNE for feature extraction.

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