Unsupervised Learning in Machine Learning > Dimensionality Reduction

Overview of Dimensionality Reduction

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Dimensionality reduction is a process of reducing the number of features in a dataset by identifying and removing redundant features. Dimensionality reduction is also known as feature selection, feature extraction, or dimension reduction.

Dimensionality reduction is the process of reducing the number of dimensions in a dataset. There are many methods that can be used for this. The most common method is PCA where we use principal components to reduce dimensionality. PCA is an unsupervised method and it does not require any labeled data. However, there are some other methods like t-SNE which require labeled data and it is supervised.

Dimensionality reduction is a technique used to reduce the number of features in a dataset so that the data can be better understood and easier to process. The main goal of dimensionality reduction is to reduce the number of features so that they can be mapped into an understandable space with fewer dimensions. The techniques used are linear and nonlinear dimensionality reduction.

Dimensionality reduction is used in various stages of machine learning. These are:

  • Dimensionality Reduction can be used during feature engineering
  • Dimensionality Reduction can be used during model training
  • Dimensionality Reduction can be used during model evaluation
  • Dimensionality Reduction can be used as part of hyperparameter optimization

Nowadays, dimensionality reduction methods are used in many fields. They are used to compress a high-dimensional data set into a low-dimensional matrix. It is done by reducing the number of variables in the data set and then using them to create new variables.

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  1. Overview of Dimensionality Reduction
    4m 25s
  2. Principal Component Analysis
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  3. Princinpal Component Analysis - Practical
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  4. Linear Discriminant Analysis
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