Principal Component Analysis (Almost) Explained on Twitter

  1. Ed Yong asked his legion of Twitter followers to explain Principal Component Analysis (PCA) in a single tweet that would be understandable to a schoolchild, here is my Storify of the results.
  2. nextgenseek
    RT @edyong209: Explain principal component analysis to a schoolchild in a tweet. Go.
  3. Symbiologica
    @edyong209 All you want for your birthday is… A quick n easy definition of principal component analysis! 🙂
  4. His followers responded, and the results were interesting, initially they ranged from vague to overly technical
  5. endless_psych
    @edyong209 PCA – Basically factor analysis yeah? Principal component analysis?
  6. blakestacey
    @edyong209 “If you have to talk about something really complicated but you can only use two or three numbers, what do you do?”
  7. joelmcglothlin
    @edyong209 Taking lots of numbers and boiling them down into as few numbers as possible.
  8. J_Liptak
    @edyong209 A colleague once summed up PCA as a way of letting data decide for itself which patterns best describe it.
  9. MarkSkopinPhD
    @edyong209 PCA is a way to explain the relationship of things that at first glance appear completely unrelated from each other.
  10. CliffordTheHutt
    @edyong209 My short answer: it’s used to ID correlated variables that are being influenced by the same underlying phenomenon.
  11. GaBioscience
    @edyong209 PCA is a German diagonal iteration reducing data noise to a murmur
  12. BioinfoTools
    @edyong209 If data shows a trend, PCA can work out which of a list of factors explain most of the trend. [ >30 characters left! 😉 ]
  13. Those tweets tended to forget about “explain to a schoolchild” aspect of the challenge, others picked up the slack
  14. holtchesley
    @edyong209 compare the height, eye color, hair length, etc. of the people around you. Where do you notice the most difference?
  15. CaldenWloka
    @edyong209 It’s like representing a road with a line on a map. The road has width and length, but the length is sufficient for most purposes
  16. alex_ander
    @edyong209 Every politician has lots of beliefs, but you can guess most if you know whether politician is liberal or conservative (1st PC)
  17. pickleswarlz
    @edyong209 PCA tells you which features best describe the differences between items in a group, like colour in a box of lollies
  18. kristenobacter
    @edyong209 Imagine collecting all the rocks in the yard, but you can only keep 10. PCA picks the ones with the most common color & sizes
  19. tomroud
    @edyong209 Best soccer teams: do they score more or have the best defense ? PCA ranks values of teams based on multiple data of this kind.
  20. fLip_uk
    RT @holy_kau: @edyong209 PCA is like picking cookies with the most chocolate chips and seeing where they came from in the jar.
  21. takingapartcats
    RT @APV2600: @edyong209 Imagine lining the crayons in a box up based on their color similarity. Now, in the other direction, separate them by the wear.
  22. I personally like this video explanation, which demonstrates that PCA can be a fairly intuitive concept
  23. Some of the more bizarre answers tended to relay the mystery and seemingly magical side of PCA
  24. ErikJCox
    @edyong209 Principal component analysis, kids, is much like a stranger bearing sweets – to be avoided at all costs. Don’t believe me? Try it
  25. jashapiro
    @edyong209 Measure everything you can. PCA will turn it into fuzzy balls.
  26. richboden
    @edyong209 Something nerds do to show off and that the rest of us secretly don’t understand but always look knowingly when it’s mentioned.
  27. ErikJCox
    @edyong209 PCA compares apples with oranges in a fruit salad kind of way.
  28. nextgenseek
    @edyong209 PCA is what you do when you have lots of numbers, but dont know what to do with them 🙂
  29. scimomof2
    @edyong209 PCA is black magic math that tells which of dudes are nerd, fun, prep or jock & explains what makes them nerd, fun, prep or jock
  30. wanderingbond
    @edyong209 Like when parents try to describe something they don’t completely understand, like sex, and draw vague diagrams that explain 27%
  31. As the challenged progressed, Ed Yong retweeted some of the best responses, with equal measure of the good and the bizarre
  32. hipparchia2
    RT @edyong209: Some of you are kicking ass at the Explain-PCA-to-a-schoolchild challenge. Others have bizarre ideas about what schoolchildren are like
  33. edyong209
    RT @aimeemax: @edyong209 PCA is a way of finding basic similarities between many different things – linking complexities by their simplest similarities
  34. edyong209
    RT @mickresearch: @edyong209 PCA of leaf shape would first give size, then fatness (width:length), wiggles of leaf edge, etc. 1st parts give largest variation
  35. edyong209
    RT @CorrinneBurns: @edyong209 PCA uncovers hidden relationships between things. For example: everyone who follows @edyong209 hates cheese, and is insomniac.
  36. edyong209
    RT @VinJLynch: @edyong209 PCA asks data how it wants us to describe it to our other friends.
  37. Some noted that his retweeting behavior, was itself an example of PCA
  38. mjberryman
    @edyong209 PCA is like RTing only the best explanations on Twitter.
  39. joe_pickrell
    @edyong209 Great, you just did PCA–you summarized many diverse tweets with two axes of variation (PCs): ass-kicky-ness and bizarreness.
  40. The end result of the challenge was a little bit of education and interest in PCA, with some mathy/nerdy entertainment thrown in for good measure
  41. pingulette
    I’ve enjoyed seeing responses to @edyong209‘s PCA definition question get weirder/more entertaining over some hours!…
  42. kitkor
    .@edyong209 Your request to explain PCA to a child reminds me of the Flame Challenge:
  43. digitalmaverick
    @edyong209 I am a teacher and I have LOVED reading how your Followers responded to that PCA problem you set #inspirational
  44. travischapman
    After all those @edyong209 retweets, I really want to learn more about PCA

4 thoughts on “Principal Component Analysis (Almost) Explained on Twitter

  1. Pingback: Eigen-who-whats? The great Twitter PCA challenge! | Appetite for Awesome

  2. Pingback: I’ve got your missing links right here (22 December 2012) – Phenomena: Not Exactly Rocket Science

  3. Pingback: Stuff we linked to on Twitter last week | Highly Allochthonous

  4. Pingback: Round-Up Ready: A Year in Blogging Edition | On a Quasi-Related Note

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