Principal Component Analysis
Read OriginalThis technical tutorial provides a step-by-step guide to Principal Component Analysis (PCA), a linear transformation technique for dimensionality reduction. It covers the core concepts, compares PCA to LDA, and details the process from data preparation and eigendecomposition to selecting components and projecting data, using examples like the Iris dataset.
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
Quoting Thariq Shihipar
Simon Willison
•
2 votes
2
Using Browser Apis In React Practical Guide
Jivbcoop
•
2 votes
3
Better react-hook-form Smart Form Components
Maarten Hus
•
2 votes
4
Top picks — 2026 January
Paweł Grzybek
•
1 votes
5
In Praise of –dry-run
Henrik Warne
•
1 votes
6
Deep Learning is Powerful Because It Makes Hard Things Easy - Reflections 10 Years On
Ferenc Huszár
•
1 votes
7
Vibe coding your first iOS app
William Denniss
•
1 votes
8
AGI, ASI, A*I – Do we have all we need to get there?
John D. Cook
•
1 votes
9
Dew Drop – January 15, 2026 (#4583)
Alvin Ashcraft
•
1 votes