Principal Component Analysis (PCA)
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- Опубликовано: 26 янв 2020
- Principal component analysis (PCA) is a workhorse algorithm in statistics, where dominant correlation patterns are extracted from high-dimensional data.
Book PDF: databookuw.com/databook.pdf
Book Website: databookuw.com
These lectures follow Chapter 1 from: "Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by Brunton and Kutz
Amazon: www.amazon.com/Data-Driven-Sc...
Brunton Website: eigensteve.com
This video was produced at the University of Washington 
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![Principal Component Analysis (PCA) [Matlab]](http://i.ytimg.com/vi/VqjJ5YYt78Y/mqdefault.jpg)
![Principal Component Analysis (PCA) [Matlab]](/img/tr.png)
What the hell. One can download your book for free?! You sir are a saint. I will work thru it and if I like it I will definitely purchase it!! (I'm pretty sure I will like it, because I like all your videos so far)
PS: I am so proud of you guys. You are bringing humanity forward with content like this being free. I encourage everyone who can to purchase content from sources like this
Prof. Brunton always delivers the best explanations on the subjects! His videos really help me a lot! Kudos!
Indeed he does ...
You explain complicated math in a brilliant way. Thank you so much
These videos are the PCA for data driven engineering!!Thank you for bringing up these series publicly!!
The best video on PCA I could find on youtube, no messy blackboards, jokes or oversimplification, just solid explanation, great job.
Steve, able to explain PCA from classical statistiscal point of view. Very clear
I logged in just for this, which I almost never do xD
I wanted to say: Thank you!
Your video series is great, enjoyful, and helps getting familiar with the topic rapidly. The same applies to the book, which you link at for free. Thank you.
Best explanation. Looking forward to video about Kernel PCA！
The alst part of the video on how SVD and PCA are related really class of its own. IT show the expert should run video lectures
So far this is the best video of PCA explanation.
Steve's explanations are excellent.
Love this series! Just bought your book
Finally, someone who explains statistics in a straight-forward way, whilst communicating in an adult like manner.
I am a phd student learning inverse scattering, your lectures help me with understanding those concept :) greetings from naples
I've watched a lot of PCA videos and this is really the best one. You're amazing!
yes he is but do visit statquest
@@TheMangz1611 Bam. Best wishes to anyone who makes teaching intuitive.
Wow, excellent explanation. Thank you so much.
This channel is amazing!
Thank you very much for this video! Learnt quite a bit from this :)
This is amazing!
Very good explanation for each symptom and its treatment
Amazing video. I did the MIT lectures about Linear Algebra (that talked about SVD) and the Andrew Ng's ML course (that talked about PCA). This video was the perfect bridge to connect the two things in a coherent manner. Thank you very much, Dr. Brunton!
Awesome! Thank you!
superb explanation. Thank you!
Very clear, excellent
Excellent ,connected ,simple
Beautifully explained ~ and Thank you so much ^^
Thank you so much. You made it really easy to understand.
Glad to hear that!
If there was a Nobel Prize in Education (which there absolutely should be), then you should absolutely win.
Amazing! Thank you!
This is so technically correct, and simultaneously so obtuse, that my intuition fuse has melted. Please consider redoing this as 3D pseudo visualizations of data subsets.
PCA clearly explained!!!
Thank you, Prof. Brunton. I have a question: supposing I have done this series of experiments with a target measure that cannot be categorized but is a continuous value, then can I use PCA?
Correct me if I'm wrong, but B transposed multiplied by B sums up the products of mean centered values, but to get the covariation we still need to divide by number of rows in X as covariation is defined as
E{(X-E(X))*(Y-E(Y))} not just sum of (X-E(X))*(Y-E(Y)) over measurements
Thank you
Wow, I saw n videos before this, beautiful explanation¡
I started watching from SVD til here and it was super helpful! Thank you so so much.
Brilliant!
Note @ 7:50 regarding CV = VD. The D here is a matrix where all the eigenvalues are on the diagonal.
Nice lecture
Do you have a patreon? How can I help support this content? Just these materials on Ch1 and 2 have been amazing. Will it extend to addiitonal chapters?
I don't, but I really appreciate the kind words! This will extend to all of the chapters eventually.
Thank you!
this is amazing
I like your explanation. Please check equation 1.26 on your databook.
It can be helpful to use the names "features" (to refer to the 'n' different pixels in a photo, or the 'n' different characteristics of rats which may predict cancer) and "snapshots" (to refer to the 'm' different measurements (e.g. people's photos, or rats)).
Then, it doesn't matter whether you have the "features" as columns or rows - Corr(feat) = feature-wise correlation matrix, where entries represent the correlation between two features, and the eigenvectors of this matrix are the "eigenfeatures". If you happen to have "features" as columns, then Corr(feat) = [X][X^T]. If you happen to have the "features" as rows, then Corr(feat) = [X^T][X].
Similarly, for the "snapshots" we have the Corr(snap) = snapshot-wise correlation matrix, where entries represent the correlation between two snapshots, and the eigenvectors of this matrix are the "eigensnapshots". Again, depending on whether the "snapshots" are in the rows or columns of X, you can find Corr(snap).
This also helps when doing PCA, as you generally wish to reduce the number of "features", and are therefore interested in determining the eigenvectors of Corr(feat). No need to sweat over how your data is organized in the matrix X, or any annoying conventions for PCA.
In short, it is easier to think of "features & snapshots" than "rows & columns".
I can't agree more. It is inconsistent in the video and the code. In the video, he emphasized that each row has to be the features collected from a single individual. If you have a 2*10000 matrix, you have 2 individuals and 2000 features. However, a matrix of 2*10000 is generated in the code, which actually means 2 features and 10000 individuals. That takes me a really long time to figure out what happened.
Best math content is always the serious and straightforward ones.. Fuck the jokers, you are the king dude
We do the last part (T=BV) in order to calculate the inner product with the principal components.
Principal Component Analysis (PCA) is a technique in statistics that simplifies complex data by identifying and emphasizing the most important patterns or features. It does this by transforming the original variables into a new set of uncorrelated variables called principal components, allowing for a more efficient representation of the data.
Hey Dr. Brunton. Awesome video yet again! I've been snooping around kaggle, and found a dataset on body performance given a host of variables. I thought i'd try using PCA to determine the most influential characteristics within the data and began working with it in matlab. I was able to get tons of outputs (a thrill unto itself) and a nice little scatter plot! However, when all was said and done I had difficulty understanding which variables were most influential by looking at the scatter plot and PCA breakdown. What should I be doing/thinking to gain that intuition? Thanks!
Amazing video. To the point and efficient.
Glad it was helpful!
very good, thanks a lot 😅
You are an angel!!!!!!!!!!!!!
He just knows it all.
Lol, not even the first principal component! :)
Nobody gonna say anything about how this man just wrote all of that backwards flawlessly?
Thanks for the great explanation! In your next video, can you please explain how you are writing backward!?
He writes forwards and then flips the video horizontally
Ha ha ha
Can you also show how to get covariance matrix from a Gaussian function results from its fit on a Gaussian looking data. Any suggestion for a book to explain this kind of stuff? Cheers.
Does the concept of cross-loadings exist in PCA like it does in EFA? If it does exist, what are the criteria to determine so?
Is this done with a glass whiteboard and the recording is mirrored?
I believe that there's a typo. The principal components are the columns of V.
In some implementations, I find that along with mean centering, standard deviation division is followed (Z-scores), does this make a difference? I believe standard deviation division is important to keep the features on the same scale (Unit Variance).
This guy is super good at writing backwards
took me a minute to realise you record this and then mirror the video, rather than learning to write backwards hahaha
If I have outliers in my dataset, can this affect the PCA?, because I have tried with cases of this type and it usually identifies a single principal component
Can you please share what software and equipment you're using for this presentation?
Everyone: Great video
Me: Wondering how he can write backwards
The data matrix is a wide matrix, so if it is already zero mean, then in this case the PC XV is equal to XU (Considering U from the SVD lecture)?
I came to learn about PCA, but now I’m just focusing on how he can write backwards so clearly.
It's a trickle on the optocordical neural network involving image inversion
So as another way to look at this, are U the scores, sigma the eigenvalues, and V the loadings?
Hi professor, Just one question. If your X matrix has samples in the rows and sample features in the columns, then the correct shouldn't be to calculate the column-means(X), instead of row-means(X), and subtract each column-value by its respective column-mean? So, each X column (feature) has mean = 0.
I think, as he explained at the beginning, this mix-up happened due to the difference in representing the data in SVD literature and PCA literature. I am rewatching this lecture after watching the next one where MATLAB demonstration is given. The code does exactly that, take column-mean of each person and then subtract. I came down in the comment section to check if somebody else had this confusion also.
hi doctor，really usefull to watch your lecture,but in the video,you have pointed out that T matrix is the principle components, however ,this is what confused me, my knowlage is that the col vector of loading are principle components, T is just transformed version of the data B. pls correct me if im wrong, thanks.
I am confused with SVD of B in step 4 , Isn't we do SVD or Eigen decomposition of C the covariance matrix? i.e. T=CV=UE, C=UEV' ? thank you
Thank so much. Please, can you make a video on Invariant coordinate selection (ICS) method ???
It will be very helpful. Thank in advance.
are you writing in the reverse order (right to left) on the board?
If we do row-wise correlation with respect to B, should it be C=B * B_T instead of B_T * B?
i agree with you
Yeah, he wrote "BTB is the covariance of the the rows of B", but I think he meant the columns (the features)
Great
Hi Steve,
There may be a tiny typo in Page#22 in your Data Driven Science book. The equation(1.26) is supposed to be $B = X - \bar X$ to represent demeaned data $X$ while it shows $B = X - \bar B$ on the book. Please correct me if I am wrong.
Please could you make a video about singular spectrum analysis?
Still confused how do we get BV=USigma🤔🤔 since Vt doesn’t cancel with V right?
4:45 here you’re summing over the elements of each row, but in the book on page 21 it say x_j = sum_i X_ij so you’re building the sum of each column. Is it a typo ?
BtB seems to calculate the cariance matrix of cols of B.
Yep, this essentially is a matrix of inner products of each column with each other.
how do you write inverted letters so quick? or is it some kind of CGI?
thnx
good explanation in general. but you really should play with matrix dimension during explanations.
you are my idol now =vvvvvvvvvvvvvvvvvvvvvvvvvv
Is it important to show 95% confidence ellipse in PCA? If my data is not drawing then what should i do ? can i used PCA score graph without 95% confidence ellipse?
Steve love your work, and your production values are fantastic - fyi, you can likely fix a lot of the contact noises from your lav mic with some relatively simple filtering and processing, and lowering the levels a bit during the recording could help avoid the distortion that crops up sometimes. If you'd like to discuss, let me know and I'll get you my contact details.
Thanks! -- always looking to improve the quality here.
Excelent teaching. I have one question tho. When you wrote the covariance matrix of the rows (6:00) because each row is a measurement vector I thought its the covariance between the measurements but then you wrote C=(BT)(B) which is the covariance of the features. Can you explain please.
From what I could find in PCA literature, it depends on what you have more of (Objects or Variables/Features). Both (BT)(B) and (B)(BT) is possible when doing PCA, and the covariance matrix you calculate depends on this (you always take the larger one).
Here I am six month later but now I understand my problem. So, we have measurment vector M and every mesurment has some features such as age, height, disease and etc. Now what we are intersted in, is to understand the distributation and the covariance of these features to workout for example joint or posterior distributions or etc. For example, the positive covariance between age and testing positive for some disease means there is a relation between these two ,the more the age the more the risk of this disease. So, we need the (BT)*B that is Cov-Var between features, then we can find the joint or posterior probablity distribution.
@@MilianoAlvez right, which means B*B is the covariance of the columns. Brunton I think accidentally wrote "rows"
Hello, can you show with examples how to curvilinear component analysis?
this is more than Awesome!! i want to ask you one question and it is here
a1=[1,23,4,51,62,7,8,43,1,29]
a2=[5,45,32,51,60,7,8,35,10,31]
a3=[13,3,64,35,36,37,48,3,31,1]
a4=[3,3,1,5,6,3,8,3,1,3]
a5=[0,3,0,5,0,0,8,0,0,1]
how can i figure out important columns (features) with eigenvalues and eigenvectors?
As we can see here , importance of a4 and a5 is negligible! but how can i find out with this concept?
I have eigenvalues and eigenvectors of this but do not know how to use them in this context ?
after finding eigenvalues and eigenvectors , i know how to find PC.Because i have seen your videos .
As i have seen in the comment section someone already asked this question . But i was not able to understand the Ans!
kindly help me out.
Should #3 be the covariance matrix of the columns rather than the row ?.
It seems to me that leads to V rows = B columns
@6:08, Can anybody confirm that C=B*BT instead of C=BT*B. That is because each row of B represents the measurement of a variable (0 mean).
Why did we calculate covariance matrix?
Thank you for the lecture, its been very helpful. On an unrelated note, how do you write backwards with such ease?
They probably just mirror the video
I was thinking the same!
@@LTForcedown no, he writes backwards.
it is a mirroring technique - he cannot write backwards with such ease
I think he is using a special technology which shows mirror image of his board in front of him
Can someone explain to me, why covariance matrix is just the inner product of B transposed B?
I was following ok until the part that started with writing the lambdas. I got lost there.
Also, how is this and the nipals algorithm related?
If the images of X are not all independent, then X is not full rank matrix.then will we have only rank number of eigen faces?
You only said about the data should have 0 mean, but what about the standard deviation? Don't we need to scale the data first by dividing each measure by its standard deviation to make sure the PCA doesn't easily overfit to direction with the largest magnitude?
Thanks for this video! What's the difference between cumulative sum of sigma instead of lamba? (11:30)
In "Principal Component Analysis (PCA) 2 [Python]" you do the cumulative sum of S (sigma elements).
Thanks!
How does he write in reverse?
doesn't C has to be B*B^T ? B^T * B is the covariance of the columns if I get this correctly
indeed, C should have to divide to 1/N ... or may be my memory is wrong
No. (and you yourself pointed out why:-).
Each column represent a single feature, e.g. "age" for the entire population.
Each row contains the features of a single individual, e.g. "age, weight, sex,...".
In order to get an estimate of , say, cov("age","weight") one has to multiply the columns,
"age"^T * "weight"
and divide by, say N-1 (roughly the number of individual samples in each column as suggested by Kieng Toan).
That is why B^T * B is due here.
May I suggest a RUclips playlist of Victor Lavrenko that actually explains the general motivation driving PCA and the specific motivation for searching for the eigenvectors of the covariance matrix.
ruclips.net/p/PLBv09BD7ez_5_yapAg86Od6JeeypkS4YM
esp. video #7
@Elad M @Kottel Kannim You're both right. B^T * B is the covariance of the columns, and that's what we want... i.e., covariance of the features (or variables). Brunton mistakenly writes "covariance of the rows of B".
At the 13’45 ‘’ mark why is the equation CV=VD? Should it be CV=DV?
Hi sir, The approach of explanation is good but the clarity of the main mathematical concept (eigenvalue and eigenvectors) lags. Thanks for sharing this awesome content. Love and respect from India.
Thanks, and I appreciate your candor
You have to watch this first. It's part of a playlist ruclips.net/video/nbBvuuNVfco/видео.html