Introduction to Dimensionality Reduction in Machine Learning

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Introduction to Dimensionality Reduction: Importance and Techniques
Updated on 18th Aug, 23 5.6 K Views

Dimensionality reduction plays a significant role in business by addressing the challenges posed by high-dimensional data. In this blog post, we will explore the importance of dimensionality reduction and introduce some popular techniques used to achieve it.

Given below are the following topics we are going to cover:

What is Dimensionality Reduction?

Dimensionality reduction is a technique used to decrease the number of input variables or features within a dataset while retaining the most important information.  It is a fundamental technique employed in the fields of data analysis and machine learning.

The primary objective is to diminish the dimensionality of the data by eliminating redundant or irrelevant features. This simplifies the data representation and enhances computational efficiency.

Real-world datasets frequently encompass a multitude of variables or features, which can pose challenges in terms of computational complexity, overfitting risks, and interpretability. 

Dimensionality reduction techniques offer remedies to these issues by transforming the original high-dimensional dataset into a lower-dimensional space. This is achieved through the creation of new variables or projections that capture the essential characteristics of the data.

The underlying goal of dimensionality reduction is to extract a concise representation of the data that maintains the inherent structure, patterns, and relationships among the data points. 

By reducing the dimensionality, the data becomes more responsive for visualization, exploration, and computation. This leads to improved efficiency, enhanced model performance, and deeper insights into the driving factors of the data.

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Why Do We Need Dimensionality Reduction?

Dimensionality reduction is essential in machine learning and predictive modeling for several reasons:

Why Do We Need Dimensionality Reduction?
  • Curse of Dimensionality: High-dimensional datasets often suffer from the curse of dimensionality. As the number of features increases, the data becomes increasingly sparse, making it difficult to obtain meaningful insights or build accurate models. Dimensionality reduction addresses this issue by reducing the number of features and improving the data’s density and interpretability.
  • Computational Efficiency: With a large number of features, the computational complexity of algorithms increases significantly. Dimensionality reduction techniques help reduce the computational burden by working with a reduced set of features, enabling faster data processing and model training.
  • Overfitting Prevention: High-dimensional datasets are more prone to overfitting, where a model fits the noise or random fluctuations in the data rather than capturing the true underlying patterns. By reducing the dimensionality, dimensionality reduction techniques help mitigate overfitting, leading to more generalizable and robust models.
  • Visualization and Interpretation: Visualizing high-dimensional data is challenging, as it is difficult to visualize data beyond three dimensions. Dimensionality reduction enables the projection of data into a lower-dimensional space, allowing for easier visualization and interpretation. It helps in identifying patterns, clusters, and relationships between variables, aiding in better understanding and decision-making.

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Features of Dimensionality Reduction

Dimensionality reduction techniques offer several key features that make them valuable in data analysis and machine learning:

  • Feature Selection: Dimensionality reduction allows for the selection of the most informative and relevant features from the original dataset. By discarding redundant or irrelevant features, it focuses on the subset of variables that contribute the most to the underlying patterns and relationships in the data.
  • Data Compression: Dimensionality reduction techniques compress the data by transforming it into a lower-dimensional representation. This compressed representation retains as much relevant information as possible while reducing the overall dimensionality of the dataset. This compression helps reduce storage requirements and computational complexity.
  • Noise Reduction: High-dimensional datasets often contain noisy or irrelevant features that can negatively impact analysis and modeling. Dimensionality reduction methods help reduce the impact of noise by removing or minimizing the influence of irrelevant features. By focusing on the most informative features, dimensionality reduction enhances the signal-to-noise ratio in the data.
  • Improved Visualization: Visualizing high-dimensional data is challenging, as human perception is limited to three dimensions. Dimensionality reduction enables the projection of data into a lower-dimensional space, typically two or three dimensions, making it easier to visualize and interpret. This visualization aids in understanding data patterns, clusters, and relationships.

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Dimensionality Reduction Techniques

There are several dimensionality reduction techniques that are commonly used in data analysis and machine learning. Let us see each of them in detail:

  • Principal Component Analysis (PCA): PCA can be a good example of dimensionality reduction as it is a widely used linear dimensionality reduction technique. It transforms the data into a lower-dimensional space by finding orthogonal directions, called principal components, that capture the maximum variance in the data. PCA preserves the most important information while reducing dimensionality.
  • Linear Discriminant Analysis (LDA): LDA is a technique commonly used in classification problems. It aims to maximize the separation between different classes while reducing their dimensionality. LDA finds linear combinations of features that best discriminate between classes.
  • Non-Negative Matrix Factorization (NMF): NMF is an unsupervised learning technique that decomposes the data matrix into non-negative factors. It extracts underlying patterns by representing the data as a linear combination of non-negative basis vectors. NMF is particularly useful for non-negative and sparse data.
  • t-distributed Stochastic Neighbor Embedding (t-SNE): t-SNE is a dimensionality reduction technique that is predominantly employed for visualization purposes. It aims to preserve the local structure of the data points in lower-dimensional space, making it suitable for visualizing clusters or groups within the data.
  • Autoencoders: Autoencoders are neural network models that learn to reconstruct the input data from a compressed representation. They consist of an encoder network that maps the input to a lower-dimensional latent space and a decoder network that reconstructs the original input. Autoencoders can learn nonlinear transformations and capture complex patterns in the data.

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Methods and Approaches of Dimensionality Reduction

There are various methods and approaches for dimensionality reduction, each with strengths and suitable scenarios. Here are some commonly used techniques:

  • Principal Component Analysis (PCA): PCA is a popular linear dimensionality reduction technique. It identifies the directions, called principal components, along which the data varies the most. By projecting the data onto a subset of these components, PCA reduces the dimensionality while preserving the maximum amount of variance in the data. PCA is effective when the data have correlated features and when linearity assumptions hold.
  • Linear Discriminant Analysis (LDA): LDA is another linear dimensionality reduction method that focuses on maximizing the separation between classes in a supervised setting. It seeks to find a linear combination of features that maximizes the ratio of between-class scatter to within-class scatter. LDA is commonly used in classification tasks to enhance the separability of different classes.
  • t-Distributed Stochastic Neighbor Embedding (t-SNE): t-SNE is a non-linear dimensionality reduction technique that emphasizes preserving local structures in the data. It maps high-dimensional data points to a lower-dimensional space while maintaining their similarities. t-SNE is often used for visualizing complex, clustered data patterns in two or three dimensions.
  • Autoencoders: Autoencoders are neural network architectures used for non-linear dimensionality reduction. They consist of an encoder network that compresses the input data into a lower-dimensional representation and a decoder network that reconstructs the original data from the compressed representation. By training the autoencoder to minimize the reconstruction error, meaningful features are learned in the bottleneck layer, effectively reducing dimensionality.
  • Manifold Learning Techniques: Manifold learning methods aim to capture the underlying structure or manifold of the data in a lower-dimensional space. Techniques like Isomap, Locally Linear Embedding (LLE), and Laplacian Eigenmaps attempt to preserve local relationships and neighborhood information. These methods are useful for capturing non-linear structures and are often applied to image and speech data analysis.
  • Feature Selection: Feature selection techniques select a subset of the original features based on their relevance and importance for the task. Methods like Recursive Feature Elimination (RFE), L1 regularization (Lasso), and correlation-based feature selection identify and retain the most informative features while discarding irrelevant or redundant ones. Feature selection is computationally efficient and can enhance model interpretability.
  • Random Projection: Random projection is a technique that uses random matrices to map high-dimensional data to a lower-dimensional space while preserving pairwise distances between the points. It is a computationally efficient method suitable for large-scale datasets but may not preserve complex non-linear structures or other techniques.

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Dimensionality Reduction Examples

Here are several examples illustrating the application of dimensionality reduction techniques in various domains:

Image and Video Processing 

Dimensionality reduction techniques, such as PCA and autoencoders, find utility in reducing the dimensionality of image and video data. This application proves beneficial for tasks like image compression, denoising, and feature extraction. By reducing dimensionality, these techniques enable a decrease in the computational complexity of image and video processing algorithms.

Text Analysis 

In natural language processing (NLP), dimensionality reduction techniques are used to extract meaningful features from text data. Methods like latent semantic analysis (LSA) and topic modeling (e.g., latent Dirichlet allocation) can reduce the dimensionality of text data, enabling tasks like document classification, sentiment analysis, and information retrieval.

Genomics and Bioinformatics

High-throughput biological data, such as gene expression data and DNA sequences, often have a large number of features. Dimensionality reduction techniques are employed to uncover patterns and reduce noise in such data. Methods like PCA and t-SNE can be used to visualize gene expression profiles or identify clusters of genes with similar expression patterns.

Recommender Systems

Dimensionality reduction is applied in recommendation systems to handle the high-dimensional nature of user-item interactions. Techniques like matrix factorization and singular value decomposition (SVD) can reduce the dimensionality of the user-item interaction matrix, enabling more efficient and accurate recommendations.

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Advantages of Dimensionality Reduction

Dimensionality reduction offers several advantages in data analysis and machine learning:

Advantages of Dimensionality Reduction
  • Improved Computational Efficiency: Dimensionality reduction techniques simplify the computational complexity of algorithms by reducing the dimensionality of the dataset. As a result, data processing and model training become faster, leading to improved efficiency in the overall analysis.
  • Enhanced Model Performance: Dimensionality reduction can help improve the performance of machine learning models. By eliminating irrelevant or redundant features, it reduces noise and focuses on the most informative variables. This can lead to more accurate predictions, reduced overfitting, and better generalization of the models.
  • Easier Data Visualization: High-dimensional data is challenging to visualize and interpret. Dimensionality reduction techniques transform the data into a lower-dimensional space, allowing for easier visualization. This enables the exploration and identification of patterns, clusters, and relationships among variables, aiding in better understanding and decision-making.
  • Noise and Outlier Removal: High-dimensional datasets often contain noisy or irrelevant features that can negatively impact the analysis. Dimensionality reduction techniques can help filter out noise and outliers, leading to cleaner and more reliable data.

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Dimensionality reduction plays a pivotal role in enhancing data analysis and machine learning tasks. In the present day, an immense volume of data is generated continuously, predominantly high-dimensional data. This data necessitates preprocessing before it can be effectively utilized. Therefore, it is essential to explore methods for managing such high-dimensional data. Dimensionality reduction offers precise and efficient approaches to preprocessing the data in question.

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