Dimension Reduction

Definition of Dimension Reduction

Dimension reduction is a data pre-processing technique that reduces the number of dimensions in a dataset while preserving most of the information. It is often used to improve performance and scalability when working with high-dimensional datasets.

What is Dimension Reduction used for?

Dimension reduction is a type of data transformation technique used in machine learning and data science. It is used to reduce the number of features or variables in a dataset while maintaining the most essential information. By reducing the number of features, it can reduce overfitting and improve the performance of machine learning models. Dimension reduction techniques can be divided into two categories: feature selection and feature extraction. Feature selection techniques select a subset of existing features in a dataset, such as selecting only the most important ones based on statistical methods. Feature extraction techniques generate new features from existing ones, such as principal component analysis (PCA) which creates new components from linear combinations of existing features. Dimension reduction can also help visualize high-dimensional datasets by projecting them onto lower-dimensional subspaces, making it easier to identify hidden patterns or relationships in the data.

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