Dimensionality Reduction

Dimensionality reduction is a set of techniques in machine learning and data science that reduces the number of random variables (features) in a dataset while preserving as much meaningful information as possible. It transforms high-dimensional data into a lower-dimensional representation, often to improve computational efficiency, reduce noise, or enable visualization. Common applications include data compression, feature extraction, and mitigating the curse of dimensionality in predictive modeling.

Also known as: DR, Dimension Reduction, Feature Reduction, Data Compression Techniques, Dimensionality Reduction Methods

Why learn Dimensionality Reduction?

Developers should learn dimensionality reduction when working with high-dimensional datasets, such as in image processing, natural language processing, or genomics, where models can become computationally expensive or overfit. It is essential for visualizing complex data in 2D or 3D plots, improving algorithm performance by removing redundant features, and preparing data for tasks like clustering or classification. For example, use it to preprocess data before applying machine learning algorithms to enhance accuracy and speed.

Learning Resources

• docs: Scikit-learn Dimensionality Reduction Documentation
• course: Coursera Machine Learning Course by Andrew Ng (includes PCA)
• video: StatQuest: Principal Component Analysis (PCA) Video
• book: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (Book Chapter on Dimensionality Reduction)
• tutorial: Towards Data Science Tutorial on Dimensionality Reduction