Implementing a Principal Component Analysis (PCA)
Read OriginalThis article provides a comprehensive, step-by-step guide to implementing Principal Component Analysis (PCA) from scratch. It explains the mathematics behind PCA, compares it with Multiple Discriminant Analysis (MDA), demonstrates the process with 3D sample data, and shows how to validate results using Python libraries like matplotlib.mlab and scikit-learn.
Comments
No comments yet
Be the first to share your thoughts!
Browser Extension
Get instant access to AllDevBlogs from your browser
Top of the Week
1
Introducing GPT-5.1 for developers
Simon Willison
•
6 votes
2
A simple explanation of the big idea behind public key cryptography
Richard Gendal Brown
•
1 votes
3
Google Antigravity Exfiltrates Data
Simon Willison
•
1 votes
4
5
Fix “This video format is not supported” on YouTube TV
David Walsh
•
1 votes
6
Tooltip Components Should Not Exist
TkDodo Dominik Dorfmeister
•
1 votes
7
llm-anthropic 0.22
Simon Willison
•
1 votes
8
GPT-5.1 Instant and GPT-5.1 Thinking System Card Addendum
Simon Willison
•
1 votes
9
Nano Banana can be prompt engineered for extremely nuanced AI image generation
Simon Willison
•
1 votes
10
Hire Me in Japan
Dan Abramov
•
1 votes