Making Sense of Principal Component Analysis, Eigenvectors and Eigenvalues
Posted2 months agoActive2 months ago
stats.stackexchange.comSciencestory
calmpositive
Debate
0/100
Principal Component AnalysisLinear AlgebraStatistics
Key topics
Principal Component Analysis
Linear Algebra
Statistics
The post shares a discussion on understanding Principal Component Analysis (PCA) through eigenvectors and eigenvalues, with a single comment indicating appreciation for the shared resource.
Snapshot generated from the HN discussion
Discussion Activity
Light discussionFirst comment
38s
Peak period
1
0-1h
Avg / period
1
Key moments
- 01Story posted
Oct 28, 2025 at 2:22 PM EDT
2 months ago
Step 01 - 02First comment
Oct 28, 2025 at 2:22 PM EDT
38s after posting
Step 02 - 03Peak activity
1 comments in 0-1h
Hottest window of the conversation
Step 03 - 04Latest activity
Oct 28, 2025 at 2:22 PM EDT
2 months ago
Step 04
Generating AI Summary...
Analyzing up to 500 comments to identify key contributors and discussion patterns
Discussion (1 comments)
Showing 1 comments
tptacekAuthor
2 months ago
This has been submitted several times before (unsurprisingly! it's an extremely good piece of technical writing) but never discussed. Let's see if the nth time is the charm.
View full discussion on Hacker News
ID: 45736725Type: storyLast synced: 11/17/2025, 8:07:20 AM
Want the full context?
Jump to the original sources
Read the primary article or dive into the live Hacker News thread when you're ready.