AI Terakoya Top › Machine Learning › Unsupervised Learning › Chapter 2: Dimensionality Reduction
🌐 EN | 🇯🇵 JP | Last sync: 2025-11-16
This chapter introduces the basics of Introduction to Dimensionality Reduction. You will learn essential concepts and techniques.
Full translation pending - Structure and navigation translated
Chapter Contents
- What is Dimensionality Reduction - Visualization and compression of high-dimensional data
- Principal Component Analysis (PCA) - Linear transformation that maximizes variance
- t-SNE - Nonlinear dimensionality reduction and visualization
- UMAP - Fast and flexible dimensionality reduction
- Application Examples - Visualization of image data and text data
← Previous: Chapter 1 Next: Chapter 3 →
Disclaimer
- This content is provided solely for educational, research, and informational purposes and does not constitute professional advice (legal, accounting, technical warranty, etc.).
- This content and accompanying code examples are provided “AS IS” without any warranty, express or implied, including but not limited to merchantability, fitness for a particular purpose, non-infringement, accuracy, completeness, operation, or safety.
- The author and Tohoku University assume no responsibility for the content, availability, or safety of external links, third-party data, tools, libraries, etc.
- To the maximum extent permitted by applicable law, the author and Tohoku University shall not be liable for any direct, indirect, incidental, special, consequential, or punitive damages arising from the use, execution, or interpretation of this content.
- The content may be changed, updated, or discontinued without notice.
- The copyright and license of this content are subject to the stated conditions (e.g., CC BY 4.0). Such licenses typically include no-warranty clauses.