· Machine Learning · 2 min read
📋 Prerequisites
- Basic understanding of data and ML concepts
🎯 What You'll Learn
- Understand what dimensionality reduction is and why it is important
- Learn different dimensionality reduction techniques like PCA, t-SNE, and UMAP
- See practical examples of dimensionality reduction in ML workflows
- Recognize how dimensionality reduction improves model efficiency and performance
Introduction
Dimensionality reduction is a technique used in machine learning and data analysis to reduce the number of input variables (features) in a dataset while retaining as much important information as possible.
1️⃣ Why is Dimensionality Reduction Needed?
✅ Many real-world datasets have high dimensionality (many features).
✅ High-dimensional data can lead to:
- Increased computation time.
- Overfitting in models.
- Difficulty in visualizing data.
- Redundant and irrelevant features.
Dimensionality reduction simplifies data, improving model performance and interpretability.
2️⃣ Types of Dimensionality Reduction
a) Feature Selection
Choosing a subset of the original features based on importance.
Example methods:
- Removing low-variance features.
- Using correlation analysis to remove redundant features.
b) Feature Extraction
Transforming data from high-dimensional space to a lower-dimensional space.
Example methods:
- Principal Component Analysis (PCA).
- t-Distributed Stochastic Neighbor Embedding (t-SNE).
- Uniform Manifold Approximation and Projection (UMAP).
3️⃣ Key Techniques
Principal Component Analysis (PCA)
✅ Linear technique that projects data into directions (principal components) capturing the most variance.
✅ Useful for retaining global structure of data.
✅ Commonly used for exploratory data analysis and preprocessing.
t-SNE
✅ Non-linear technique focusing on preserving local structure in data.
✅ Useful for visualizing high-dimensional data in 2D/3D.
✅ Computationally expensive, best suited for visualization, not preprocessing.
UMAP
✅ Non-linear technique like t-SNE but faster and scalable to larger datasets.
✅ Preserves both local and global structures well.
✅ Useful for visualization and as a preprocessing step for clustering.
4️⃣ Practical Example: Visualizing MNIST Digits
The MNIST dataset has 784 features (28x28 images).
✅ Using PCA, you can reduce it to 50 components for faster training.
✅ Using t-SNE or UMAP, you can visualize the data in 2D, revealing clusters of different digits for understanding the data structure.
5️⃣ Benefits of Dimensionality Reduction
✅ Improves model performance: Reduces noise and irrelevant features, lowering the risk of overfitting.
✅ Faster training: Less computational cost with fewer features.
✅ Better visualization: Enables 2D/3D visualizations of complex data.
✅ Storage efficiency: Smaller datasets require less storage.
Conclusion
Dimensionality reduction is a critical step in many ML workflows, allowing you to:
✅ Simplify high-dimensional data.
✅ Improve model interpretability and performance.
✅ Visualize data for insights and exploratory analysis.
What’s Next?
✅ Apply PCA on a dataset in your workflow.
✅ Use t-SNE or UMAP to visualize your high-dimensional data.
✅ Continue your structured learning on superml.org.
Join the SuperML Community to discuss dimensionality reduction techniques and see real examples from fellow learners.
Happy Learning! 🌀