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Linear Discriminant Analysis: Definition, Working, and Applications

Linear Discriminant Analysis: Definition, Working, and Applications

At the end of this blog, you will have a solid understanding of what LDA is, how it works, and how it can be applied, making it a valuable addition to your data analysis toolkit.

Table of Contents:

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What is Linear Discriminant Analysis?

Linear discriminant analysis (LDA) is a supervised learning algorithm used for classification and dimensionality reduction in machine learning. It aims to find a linear combination of features that best separates different classes in a dataset. 

LDA maximizes the distance between class means while minimizing the spread within each class. By projecting data points onto this discriminative axis, LDA reduces dimensionality and helps classifiers make more accurate predictions.

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Example of Linear Discriminant Analysis

Here is a simple example of how LDA can be used for classification:

how LDA can be used for classification

Consider a collection of emails that we’re aiming to categorize into “spam” and “non-spam”. LDA can serve as a powerful tool for this task. The process begins by segregating our email dataset into two distinct categories: those that are spam and those that aren’t. Using LDA, we then seek the optimal linear combination of email features that maximizes the separation between these two categories.

Upon successfully training our model using this method, it’s equipped to evaluate and categorize new incoming emails. For each new email, we determine its linear score based on our model. By comparing this score against a pre-set threshold, we can classify the email. If the score surpasses the threshold, the email is labeled “spam”. On the other hand, if it’s below, it’s deemed “non-spam”.

What is Dimensionality Reduction?

Dimensionality reduction involves reducing the number of variables or features in a dataset without losing crucial information. It’s essential for simplifying complex datasets, helping in visualization, and improving computational efficiency. 

Linear discriminant analysis is an example of a dimensionality reduction technique that aims to find a lower-dimensional space where classes in the data are well-separated, making it valuable for classification tasks and data analysis.

Fisher’s Linear Discriminant

Fisher’s linear discriminant (FLD) is a powerful supervised learning method utilized for classification and dimensionality reduction in machine learning. It identifies a linear combination of features that optimally segregates classes within a dataset. FLD achieves this by projecting data onto a lower-dimensional space, maximizing class separation. 

FLD represents a specific strategy within LDA where the data follows a Gaussian distribution and class covariance matrices are identical. It frequently serves as an initial step for dimensionality reduction before employing LDA, particularly when dealing with high-dimensional datasets.

Linear Discriminant Analysis for Multiple Classes

LDA can be adapted for multi-class classification by employing a one-vs-rest strategy. This involves training separate LDA classifiers for each class against all other classes combined. For instance, in a scenario with three classes (A, B, and C), three LDA classifiers are trained: one for distinguishing class A from the rest, another for class B, and a third for class C.

To classify new data points, each LDA classifier predicts the probability of the data point belonging to its respective class. The class with the highest predicted probability is then assigned to the data point, enabling effective multi-class classification.

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Why Use Linear Discriminant Analysis?

Linear discriminant analysis is a valuable technique in various fields of machine learning and data analysis for several reasons.

Below, we have highlighted some of the reasons why using LDA is important:

  • Dimensionality Reduction: LDA is primarily used for reducing the dimensionality of a dataset while preserving as much class discrimination as possible. It projects high-dimensional data onto a lower-dimensional space, which can be especially valuable when dealing with datasets with many features. This dimensionality reduction can lead to more efficient and faster machine learning models.
  • Feature Extraction: LDA provides a systematic way to extract the most discriminative features in a dataset. By finding linear combinations of features (the discriminant features) that maximize the separation between different classes, LDA helps in focusing on the most relevant information for classification or visualization.
  • Improving Classification Accuracy: LDA is a supervised learning technique, meaning it takes into account class labels during training. This results in improved classification accuracy compared to unsupervised dimensionality reduction techniques like principal component analysis (PCA), which do not consider class information.
  • Data Visualization: LDA can be used to visualize data by reducing it to a lower-dimensional space while maintaining class separation. This is especially useful when you want to visualize high-dimensional data and understand the underlying structure of the classes or categories.
  • Handling Multiclass Problems: LDA can handle multi-class classification problems with ease. It projects data into a space where the classes are well-separated, making it suitable for distinguishing among multiple classes.
  • Reducing Overfitting: By reducing the dimensionality of the feature space, LDA can help mitigate the risk of overfitting, which is particularly important in machine learning tasks when working with high-dimensional data.
  • Assumption of Normality: LDA assumes that the data within each class follows a multivariate Gaussian distribution. If this assumption holds true, LDA can be highly effective. However, even when this assumption is not fully met, LDA can still provide valuable insights and useful results.
  • Interpretability: The discriminant features obtained through LDA are linear combinations of the original features. This linear nature makes it easy to interpret the contributions of each feature to the classification.
  • Applications: LDA has a wide range of applications, including image recognition, text classification, bioinformatics, face recognition, and many other fields where dimensionality reduction and classification are essential.

How Does Linear Discriminant Analysis Work?

Linear discriminant analysis, or LDA, works by enhancing class separability through dimensionality reduction.

Below, we have highlighted a detailed explanation of how LDA works:

  • Projecting Data for Separation
    • LDA aims to find a linear combination of features that maximizes the distinction between classes.
    • It identifies the optimal linear coefficients to create this combination.
    • This linear combination forms a discriminant function that characterizes the separation between classes.
  • Transforming into a New Space
    • The data is projected onto this linear combination or discriminant function, effectively transforming it into a lower-dimensional space.
    • In this new space, the classes are ideally more distinct and less overlapping than in the original feature space.
  • Supervised Learning with Labeled Data
    • LDA is a supervised learning algorithm, which means it necessitates a labeled dataset for training.
    • This labeled dataset consists of data points already assigned to specific classes.
  • Feature Discrimination Learning
    • LDA learns which features or attributes are most discriminative in distinguishing between the classes.
    • It identifies the features that contribute the most to class separation during training.
  • Optimal Projection for Maximized Separation
    • Once trained, LDA uses the learned features to project new data points onto the lower-dimensional space.
    • The projection is carried out according to the linear coefficients identified earlier.
  • Classification of New Data
    • To classify new data points, LDA projects them into the same lower-dimensional space.
    • The algorithm assigns the new data point to the class with the nearest mean vector in this transformed space.
  • Decision Boundary
    • LDA establishes a decision boundary in the reduced-dimensional space.
    • This decision boundary is typically a linear hyperplane that maximizes the separation between classes.

Difference Between LDA and PCA

Linear discriminant analysis (LDA) and principal component analysis (PCA) are both dimensionality reduction techniques, but they serve different purposes and are used in different contexts. Let’s discuss the differences between LDA and PCA in various aspects:

AspectLinear Discriminant Analysis (LDA)Principal Component Analysis (PCA)
ObjectiveSupervised technique that focuses on class separationUnsupervised technique that focuses on variance
Nature of ProblemTypically used for classification tasksUsed for dimensionality reduction or noise reduction
GoalMaximize the separation between classesMaximize variance along the principal components
Input RequirementsRequires class labels for each data pointDoes not require class labels
Linearity AssumptionAssumes linear relationships between featuresAssumes linear relationships between features
Dimensionality ReductionReduces dimensions to (n_classes – 1) dimensionsReduces dimensions to any desired number

How to Prepare Data from LDA

To prepare data for linear discriminant analysis in machine learning, follow the given steps:

  • Identify Classification Problems: LDA is primarily used for classification tasks, where you aim to categorize data into different classes. It works well for both binary (two classes) and multi-class (more than two classes) classification problems.
  • Check for Gaussian Distribution: LDA assumes that the input variables follow a Gaussian (normal) distribution. It’s essential to assess the univariate distribution of each feature and transform it if needed to approximate a Gaussian distribution. 

For example, you can apply logarithmic or square root transformations to data with exponential distributions and use the Box-Cox transformation for skewed distributions.

  • Remove Outliers: Outliers can significantly impact the performance of LDA. They can skew essential statistics like the mean and standard deviation, affecting class separation. It’s advisable to detect and remove outliers from your dataset as a preprocessing step.
  • Standardize Data: LDA assumes that all input variables have the same variance. To meet this assumption, standardize your data by subtracting the mean and dividing by the standard deviation. 

This transformation ensures that the mean of each feature becomes 0 and the standard deviation becomes 1. Standardization helps LDA perform optimally and ensures that no variable dominates the others due to differences in scale.

What are the Extensions of LDA?

LDA has several variations and extensions. They are quadratic discriminant analysis, flexible discriminant analysis, and regularized discriminant analysis. Let us discuss this in detail:

  • Quadratic Discriminant Analysis (QDA): QDA relaxes the assumption of equal covariance matrices across classes. Instead, QDA allows each class to have its own covariance matrix, which can better capture the inherent differences between the classes. This can lead to improved classification performance, especially when the classes have different shapes or orientations in the feature space.
  • Flexible Discriminant Analysis (FDA): FDA extends LDA by allowing non-linear transformations of the input variables. This can be useful when the classes are not linearly separable, as LDA is limited to linear decision boundaries. FDA can capture more complex relationships between the variables and improve classification accuracy in such cases.
  • Regularized Discriminant Analysis (RDA): RDA addresses the issue of overfitting in LDA, which occurs when the model learns the training data too well and fails to generalize well to unseen data. RDA introduces regularization terms into the LDA model, which penalizes large model coefficients and helps prevent overfitting. This can improve the model’s generalization performance and reduce the risk of poor classification on new data.

How to Implement LDA Using Scikit-Learn

Here is a step-by-step guide on implementing linear discriminant analysis using Scikit-learn:

  • Import Libraries

Start by importing the necessary libraries:

import numpy as np
import pandas as pd
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
  • ‘numpy’ and ‘pandas’ for data manipulation.
  • ‘LinearDiscriminantAnalysis’ from Scikit-learn for LDA.
  • Prepare Data

Load your dataset into a pandas DataFrame and separate the features (X) from the target variable (y). Ensure your data is clean and preprocessed if needed.


# Load dataset into a pandas DataFrame
data = pd.read_csv('data.csv')
# Separate features and target variable
X = data[['feature1', 'feature2', ...]]  # Replace with actual feature names
y = data['target_variable']
  • Create and Train LDA Model

Instantiate the LinearDiscriminantAnalysis class and fit the model to your training data.


# Create LDA model
lda_model = LinearDiscriminantAnalysis()
# Train LDA model on training data
lda_model.fit(X, y)
  • Make Predictions

You can now use the trained LDA model to make predictions for new data points.


# New data point to predict
new_data = np.array([[feature1_value, feature2_value, ...]])  # Replace with actual feature values
# Predict class label for the new data point
predicted_label = lda_model.predict(new_data)
  • Evaluate Model Performance

To assess the performance of your LDA model, use appropriate evaluation metrics like accuracy, precision, recall, and F1-score. You can do this on a held-out test set or use cross-validation techniques to ensure your model generalizes well.

Applications of LDA

LDA finds various applications in different sectors for its ability to improve data analysis and dimensionality reduction. In the following points, we will talk about some of the popular applications of it.

  • Face Recognition: LDA is widely used in face recognition systems. It helps extract the most distinguishing facial features, improving recognition accuracy. By reducing the dimensionality of facial data, LDA simplifies the task of matching faces to individuals, making it valuable for security and authentication.
  • Medical Diagnosis: LDA plays a crucial role in medical diagnosis. It helps in developing models for disease classification based on patient data. For instance, LDA can assist in diagnosing diseases like diabetes by analyzing factors such as blood sugar levels, weight, and age. It provides valuable insights for early disease detection and personalized treatment.
  • Text Classification: In natural language processing, LDA is employed for text classification tasks. It helps in categorizing the documents, such as news articles, customer reviews, or emails, into relevant topics or sentiments. This is instrumental in information retrieval, recommendation systems, and sentiment analysis.


LDA effectively combines the principles of dimensionality reduction and classification, optimizing the separation between classes. Through an example of classifying emails as spam or not, we can see how LDA works to extract crucial features for accurate categorization. 

LDA stands as a versatile tool for its ability to filter essential information from high-dimensional datasets, making it a go-to choice in numerous fields, boosting classification accuracy, and streamlining the decision-making process.

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About the Author

Principal Data Scientist

Meet Akash, a Principal Data Scientist who worked as a Supply Chain professional with expertise in demand planning, inventory management, and network optimization. With a master’s degree from IIT Kanpur, his areas of interest include machine learning and operations research.