What is Dimensionality reduction

Dimensionality Reduction: A Powerful Tool for Data Analysis

Dimensionality reduction is a technique that's frequently used in data analysis to reduce the number of variables and make it easier to visualize and analyze data. It's an essential tool for machine learning and data mining, as it helps to simplify complex data structures and provides insights into the relationships between variables. In this article, we'll explore what dimensional reduction is, how it works, and some of the methods used in the field.

What is Dimensionality Reduction?

In data analysis, dimensionality refers to the number of variables or features that impact a data set. For example, a data set might include variables like age, income, gender, education level, and job title. Each variable represents a dimension, and the more dimensions there are, the more complex the data becomes. Dimensionality reduction aims to reduce the number of variables while retaining as much of the original information as possible.

Dimensionality reduction has several applications, such as:

  • Simplifying complex data structures
  • Visualizing high-dimensional data
  • Reducing the computational complexity of machine learning models
  • Finding relationships between variables
  • Creating more efficient and accurate predictive models

There are several types of dimensionality reduction techniques, including:

Feature Selection

Feature selection is a technique where the most important features or variables in a data set are selected and the less significant ones are eliminated. This technique reduces the dimensionality of the data by removing unnecessary variables, which in turn:

  • Reduces the complexity of the data
  • Improves the efficiency of predictive models
  • Improves the accuracy of predictive models

Some of the commonly used techniques to perform feature selection are:

  • Filter Method
  • Wrapper Method
  • Embedded Method
Filter Method

The filter method involves evaluating the individual features in a data set and selecting the most important ones. This is done by assigning a score to each feature based on its relevance to the output variable.

Some of the popular techniques used in the filter method are:

  • Correlation Coefficient
  • Chi-Square Test
  • ANOVA Test
Feature Extraction

Feature extraction is a technique where high-dimensional data is transformed into a low-dimensional space. It involves creating new features that capture the essential information in the original data while reducing its dimensionality. Feature extraction has several advantages:

  • Reduces noise and redundancy in the data
  • Improves the efficiency of predictive models
  • Improves the accuracy of predictive models

Some of the commonly used techniques for feature extraction are:

Methods of Dimensionality Reduction

Now, let's take a closer look at some of the most popular methods of dimensionality reduction.

Principal Component Analysis (PCA)

PCA is a popular method of dimensionality reduction that aims to transform a high-dimensional data set into a lower-dimensional one, while retaining as much of the variance in the original data as possible. The idea behind PCA is to identify the linear combinations of the original variables that explain the most variance in the data.

PCA involves a mathematical process that transforms the data into a set of orthogonal components called principal components. These components are sorted based on the amount of variance they account for, with the first component explaining the most variance.

PCA is frequently used in image recognition, data compression, and text mining, among other things. It's also used in data visualization, where it's used to represent high-dimensional data in a two- or three-dimensional space.

Linear Discriminant Analysis (LDA)

LDA is a type of supervised learning algorithm that's used to reduce the dimensionality of data sets while preserving the separation between classes. It works by maximizing the ratio of the between-class variance to the within-class variance.

In LDA, the data is projected onto a lower-dimensional space, and the information is maintained by selecting the dimensions that account for the most variance between classes. The result is a lower-dimensional representation of the data that retains the most important information.

LDA is frequently used in image processing, where it's used to classify images based on their characteristics. It's also used in face recognition and other biometric applications.

t-distributed Stochastic Neighbor Embedding (t-SNE)

t-SNE is a popular dimensionality reduction technique that's used to visualize high-dimensional data in a low-dimensional space. It works by first calculating the probability distribution of the data in the high-dimensional space and then mapping that distribution to a low-dimensional space.

t-SNE is particularly useful for visualizing data sets that have a complex structure and are difficult to analyze using other techniques. It's frequently used in machine learning, data mining, and bioinformatics.

Independent Component Analysis (ICA)

ICA is a method of separating a multivariate signal into independent, non-Gaussian components. It's frequently used in signal processing, image analysis, and blind source separation.

ICA aims to identify statistically independent sources from a mixture of signals. It works by minimizing the mutual information between the sources and maximizing the non-Gaussianity of the components. The result is a set of independent components that are easier to analyze and interpret than the original data.


Dimensionality reduction is a powerful tool that's frequently used in data analysis to simplify complex data structures, visualize high-dimensional data, and improve the efficiency and accuracy of predictive models. In this article, we've explored the two main techniques used in dimensional reduction, feature selection and feature extraction. We've also looked at some of the popular methods of dimensional reduction, including PCA, LDA, t-SNE, and ICA. As the amount of available data continues to grow, it's important that we continue to develop and refine these techniques to make sense of the data and gain valuable insights.