Dimensionality Reduction

In Machine Learning, when machine captures data, they find random data. Then machine learning uses dimensionality reduction or dimension reduction is the process for 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. It can be further divided into 5 types.

  • Principal component analysis (PCA)
  • Kernel PCA
  • Graph-based kernel PCA
  • Linear Discriminant Analysis (LDA)
  • Generalized discriminant analysis (GDA)

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