15 Dimensionality Reduction in Machine Learning Techniques To Try!

Dimensionality reduction in machine learning, using techniques like t-SNE and Autoencoders, simplifies data while preserving key patterns for efficiency and interpretability. This process not only enhances model efficiency but also lowers computational costs and boosts interpretability.

In this blog, we’ll dive into 15 key dimensionality reduction in machine learning techniques, from traditional methods like PCA to deep learning approaches. These strategies can optimize your machine learning models and make handling high-dimensional data more manageable and effective.

Top 15 Techniques for Dimensionality Reduction in Machine Learning?

Dimensionality reduction in machine learning is a crucial technique that helps in managing high-dimensional datasets, often leading to better performance and improved model interpretability. The two primary methods for dimensionality reduction are Feature Selection and Feature Extraction, both of which aim to reduce the number of features.

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Here’s a brief idea of how feature reduction techniques work in machine learning.

What Are Feature Selection Techniques?

Feature selection is a technique used in machine learning to identify and select a subset of the original features in the dataset without altering or combining them. The aim is to retain only the most relevant and significant features while discarding redundant or irrelevant ones. This step is crucial in building efficient machine learning models, as it reduces overfitting, improves model accuracy, and minimizes computational cost.

Feature selection techniques can be broadly classified into three categories: Filter Methods, Wrapper Methods, and Embedded Methods. Each approach uses a different mechanism to assess the importance of features.

1. Filter Methods

Filter methods assess the relevance of features using statistical techniques independently of any machine learning model. These methods typically rank the features based on their individual importance or correlation with the target variable and discard the least relevant ones.

Here are some examples of filter methods.

• Correlation coefficient analysis

Correlation coefficient analysis measures the strength and direction of the linear relationship between two variables. The correlation coefficient (usually Pearson’s r) ranges from -1 to 1, where values close to 1 or -1 indicate strong relationships and 0 indicates no relationship.

It helps identify highly correlated features that may be redundant in machine learning models.

• Chi-square test

The chi-square test determines if there is a significant association between two categorical variables. The technique compares observed frequencies with expected frequencies under the assumption of independence. A high chi-square value indicates a significant relationship between the variables.

It is used in categorical data analysis, such as selecting features in classification problems.

• Information gain

The information gain technique measures the effectiveness of an attribute in classifying a dataset based on the reduction in entropy. The feature that has the highest information gain (or greatest reduction in uncertainty) is considered the most important.

It is mainly used in decision trees to select the most informative features for splitting nodes.

Strengths of Filter Methods:

• Computationally inexpensive and fast.
• Independent of any learning model, which makes them versatile.
• Effective for large datasets with many features.

Limitations of Filter Methods:

• They may miss interactions between features that are only revealed when considered together in a model.

2. Wrapper Methods

Wrapper methods evaluate the performance of a feature subset by actually training a machine learning model and assessing its accuracy. These methods are more computationally expensive but tend to provide better performance as they consider feature interactions and model performance during the selection process.

Here are some important wrapper methods.

• Recursive Feature Elimination (RFE)

The RFE technique recursively removes the least important features and builds the model again to identify the most significant features. RFE trains a model, ranks the features, removes the least important one, and repeats the process until the desired number of features is selected.

It is used with any machine learning model, typically regression or classification models, to maximize model performance.

• Sequential Feature Selection

It selects features by sequentially adding (forward selection) or removing (backward elimination) features based on model performance. In forward selection, one feature is added at a time and then evaluated. In backward elimination, features are removed one by one based on the model’s performance.

It is mainly used to find the best subset of features, balancing performance and simplicity.

Strengths of Wrapper Methods:

• More accurate in selecting the right features since they are tailored to the specific model.
• Can capture interactions between features.

Limitations of Wrapper Methods:

• Highly computationally intensive, especially for large datasets.
• Can lead to overfitting if the model performance is not properly validated.

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Also Read: How to Choose a Feature Selection Method for Machine Learning

3. Embedded Methods

Embedded methods perform feature selection as part of the model training process. These methods take into account the relationship between features and the target during the learning phase, making them both efficient and effective.

You can check these important embedded methods.

• Lasso Regression

Lasso regression performs both feature selection and regularization to improve the model’s accuracy and interpretability. Lasso adds a penalty term to the linear regression cost function, forcing some feature coefficients to be zero, thus performing automatic feature selection.

It is mainly used in linear models for feature selection, especially when dealing with high-dimensional data.

• Tree-based feature selection

The tree-based models (like decision trees and random forests) rank and select important features based on their contribution to reducing model error.

Tree-based models measure feature importance based on how well features split the data to remove impurities. Features with higher importance scores are selected.

It is commonly used in classification and regression tasks, particularly when working with structured data.

Strengths of Embedded Methods:

• Computationally efficient as feature selection happens during model training.
• Captures feature interactions effectively.
• Produces robust models with fewer hyperparameters to tune.

Limitations of Embedded Methods:

• May not work well with models that don’t naturally provide feature importance (e.g., KNN, SVM).
• Tends to be model-specific, meaning the selected features may not generalize well to other models.

What Are Feature Extraction Techniques?

eature extraction involves transforming the original features into a new set of features by combining or summarizing them. The goal is to capture the most important information while reducing the number of dimensions. Feature extraction is especially useful when you need to reduce complexity while preserving essential patterns in the data.

Feature extraction techniques are categorized into linear methods (which assume linear relationships between features) and non-linear methods (which capture more complex, non-linear relationships).

Here are some popular feature extraction techniques.

1. Linear Methods

Linear methods work by projecting the data into a new space where the relationship between the features and the target variable can be captured in a linear manner. These methods are easy to interpret and computationally efficient.

Here are some of the examples of linear methods.

• Principal Component Analysis (PCA)

The PCA dimensionality reduction technique reduces the number of features in a dataset while preserving as much variance (information) as possible. 

It identifies the directions (principal components) in which the data has the highest variation and projects the data onto a smaller set of dimensions along these directions. It is mainly used in unsupervised learning tasks.

It is used in cases such as image compression to reduce the complexity of datasets with many features.

• Linear Discriminant Analysis (LDA)

LDA technique simplifies data by focusing on the features that best distinguish different categories. It helps in better classification by highlighting the most important differences. 

LDA projects data onto a lower-dimensional space by maximizing the distance between class means and minimizing the variance within each class.

LDA is mainly used in pattern recognition, especially in face recognition and speech recognition.

• Singular Value Decomposition (SVD)

It is a matrix factorization technique that decomposes a matrix into the product of three matrices.

It is mainly used in fields like signal processing, machine learning, and natural language processing. 

Strengths of Linear Methods:

• Easy to interpret and computationally efficient.
• Works well for datasets where features have linear relationships.

Limitations of Linear Methods:

• Limited in capturing complex non-linear relationships.
• May not perform well when the data lies on a non-linear manifold.

2. Non-Linear Methods

Non-linear methods identify complex patterns and relationships in the data that linear methods can miss. They are more powerful but expensive to implement.

Here are some of the examples of non-linear methods.

• t-SNE

t-SNE is a non-linear dimensionality reduction technique that visualizes high-dimensional data in 2D or 3D. It reduces the divergence between probability distributions of pairwise similarities in the original high-dimensional space and the lower-dimensional space. It preserves local structures but not global structures.

t-SNE is usually used in visualizing clusters in high-dimensional datasets like image or text data.

• UMAP

UMAP technique is similar to t-SNE but is faster and better at preserving both local and global structures. UMAP models the data as a fuzzy topological structure and makes a low-dimensional representation by optimizing the preservation of these structures. 

It is used in cases such as manifold learning and data visualization.

• Autoencoders:

Autoencoders compress and then reconstruct data, effectively reducing dimensionality. It consists of an encoder, which compresses the input data into a smaller representation (latent space), and a decoder, which reconstructs the data from the compressed form.

The autoencoder technique is usually used for feature extraction in images and text data.

• Kernel PCA

Kernel PCa uses kernel methods to perform non-linear dimensionality reduction. Kernel PCA maps the data to a higher-dimensional space where linear separation is easier and then performs PCA in this new space.

It is suitable for use in datasets with complex, non-linear structures like images or time series.

• Isomap

The isomap technique generalizes Multi-dimensional Scaling (MDS) by incorporating geodesic distances to preserve the global structure. 

Isomap first computes the shortest path between all pairs of points in a graph and then performs classical MDS on these distances to obtain a lower-dimensional embedding.

It is mainly used in non-linear datasets, such as in image or 3D shape analysis.

Also Read: Feature Extraction in Image Processing: Image Feature Extraction in ML

After a brief understanding of linear and non-linear techniques, let’s explore the difference between the two.

Strengths of Non-Linear Methods:

• Can capture complex, non-linear relationships that linear methods miss.
• Ideal for high-dimensional data with intricate patterns.

Limitations of Non-Linear Methods:

• Computationally expensive and often slower than linear methods.
• More challenging to interpret and requires careful tuning.

Also read: 25 Powerful Machine Learning Applications Driving Innovation in 2025

To select the appropriate dimensionality reduction technique in machine learning, consider the data complexity, goals, and the computational resources required.

How to Choose the Right Dimensionality Reduction in Machine Learning Technique?

Feature selection and feature extraction are two fundamental techniques in dimensionality reduction in machine learning, each suited to different types of datasets and tasks. Feature selection aims to retain the most relevant features from the original dataset, whereas feature extraction transforms the data into a smaller, more concise set. 

Here's a concise breakdown of when to use each method:

Also read: Top 6 Techniques Used in Feature Engineering [Machine Learning]

Let's explore the advantages and disadvantages of dimensionality reduction in machine learning to understand its impact on model performance.

Advantages and Disadvantages of Dimensionality Reduction in Machine Learning

Dimensionality reduction in machine learning simplifies high-dimensional datasets by reducing the number of features while retaining critical patterns. It enhances model performance, reduces computation time, and enhances interpretability. 

However, it comes with trade-offs, such as potential data loss and compromised accuracy. 

Below is a concise summary of the advantages and disadvantages:

Also read: 25+ Open Source Machine Learning Projects to Explore in 2025 for Beginners and Experts

Enhance Your Machine Learning Career with upGrad!

Dimensionality reduction techniques, such as PCA and t-SNE, are crucial for enhancing machine learning model performance and improving data visualization. To optimize your models, carefully select the appropriate technique based on your data type and the specific task at hand. 

A common challenge is balancing model performance with computational efficiency when using complex non-linear methods. upGrad’s machine learning courses provide valuable insights into dimensionality reduction and hands-on experience with practical data. 

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Reference:
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