Data Mining - (Dimension|Feature) (Reduction)

Thomas Bayes


In machine learning and statistics, dimensionality reduction is the process of reducing the number of random variables (features) under consideration and can be divided into:

This methods are some called “Model selection methods”.

They are an essential tool for data analysis, especially for big datasets involving many predictors.

In dimensionality reduction, the goal is to select/retain a subset of features while still retaining as much of the variance in the dataset as possible.



  • random projections
  • feature hashing

Documentation / Reference

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