dimensionality reduction algorithms

Dimensionality Reduction Algorithms

Dimensionality reduction is the process of reducing the number of random variables under consideration by obtaining a set of principal variables. It can be divided into feature selection and feature extraction.

  • Principal Component Analysis (PCA): Reduces the dimensionality of the data by selecting the most important features that capture maximum information.
  • Principal Component Regression (PCR): PCR is a regression technique that is based on PCA. It’s used for building predictive models with high-dimensional data.
  • Partial Least Squares Regression (PLSR): PLSR is a technique that combines features to reduce dimensionality.
  • Sammon Mapping: Reduces dimensionality while preserving the structure of inter-point distances in high-dimensional space.
  • Multidimensional Scaling (MDS): Aims to place objects in N-dimensional space so that their distances are preserved.
  • Projection Pursuit: Finds the most interesting projection for high-dimensional data.
  • Linear Discriminant Analysis (LDA): Used to find a linear combination of features that characterizes or separates two or more classes of objects or events.
  • Mixture Discriminant Analysis (MDA): A generalization of LDA that involves a mixture of Gaussians.
  • Quadratic Discriminant Analysis (QDA): A variant of LDA where each class uses its own estimate of variance.
  • Flexible Discriminant Analysis (FDA): Combines features in a non-linear way to maximize class separability.
  • Regularized Discriminant Analysis (RDA): Combines LDA and QDA.
  • Partial Least Squares Discriminant Analysis: A variant of PLSR used in classification.

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