Let's explore the math behind principal component analysis! --- Like, Subscribe, and Hit that Bell to get all the latest videos from ritvikmath ~ --- Check out my Medium: / ritvikmathematics
Пікірлер: 165
@loveena4193 жыл бұрын
Finally, a video that explains the math behind PCA so clearly. Went through all the other videos and it helped a lot! Thank you!
@pigtowndanzee4 жыл бұрын
Love your teaching style. Keep these videos coming!
@vinceb80413 жыл бұрын
I've been wrestling to get all intuitional and computational components for doing pca for a while, and seeing it all come together here helps tremendously! Great as always, 10/10 video :)
@qaarloshilaal27783 жыл бұрын
Thanks infinitely for all your videos, you're literally the best at explaining these concept in a clear and excellent way in order to continue with what we have to study/ do! Huge respect man.
@ritvikmath3 жыл бұрын
You're very welcome!
@joachimguth62264 жыл бұрын
Very well presented. You are a great teacher. Hopefully you are going to cover the entire AI space.
@ritvikmath4 жыл бұрын
That is the goal!
@warrenbaker41243 жыл бұрын
@@ritvikmath Oh wow!!! I'm so happy to see you're taking this on. I'm a huge fan and this is a real highlight for me. Thanks for all you do!!
@Moiez101 Жыл бұрын
@@ritvikmath i fully support that goal! I just started with data science bro. Loving your videos, you're a great teacher.
@paulbrown58393 жыл бұрын
This is a very strong video. It requires proper study. I hope you do more of this great stuff. Thank You!
@mathematicality2 жыл бұрын
Simple and straight to the point. aBsolutely welldone!
Thanks Ritvik. Excellent explanation of PCA. Good job, well done!
@shivamkak79819 ай бұрын
Such a well curated explanation of PCA, thanks so much!
@bhajman1233 жыл бұрын
Byfar the most accessible description of pca...finally was able to clearly connect the covar matrix and the eigen values to variance maximization
@jaivratsingh9966 Жыл бұрын
Simply excellent!
@robertbillette46712 жыл бұрын
Like everyone else has mention, amazing clarity and style.
@sandeepc28334 жыл бұрын
Cleared most of my doubts. Thanks a lot.
@martinw.97862 жыл бұрын
Thank you very much for the explanations - very very well done. Your references to the mathematical backround is key!
@kakabudi2 жыл бұрын
Really great video! Thanks for explaining this concept wonderfully!
@RobloxCatGirl2 ай бұрын
Thank god I found your channel. I am studying masters degree in computer science in a prestigious university and cost me a lot of money but your channel is very useful to dig deeper and understand many things. Stay on the good work!
@paulntalo14253 жыл бұрын
You have made it clear. Thank you
@BleachWizz3 жыл бұрын
I'm loving your content, you're showing a part of math that is not usually shown. The part where you actually use it, where you make your choices and why are you choosing them. Like it's nice to understand the equations and why it gives you a 0 on the sweet spot, but it's also nice to remind that it not only works but it was build to work with that intention. So in the end you still need to figure out how do you get your problem to fit in one of those, what can you choose in these big generic operations to fit it into your problem.
@ritvikmath3 жыл бұрын
Thanks for the feedback! I do try to focus a lot more on the "why" questions rather than the "how" questions.
@shashanksundi56692 жыл бұрын
Just perfect !! Thank you :)
@amaramar49693 ай бұрын
I had to go thru the prerequisite videos to clarify my concepts first, but after that this PCA explanation is amazing! I think you are equivalent to 10 college professors out there in terms of teaching skills. I hope you get that proportion money and the college professors feel ashamed and work harder to catchup to your standards. Again, amazing!
@TamNguyen-qi8di3 жыл бұрын
Dear rivitmath, Thank you so much sir for your clear explanation. Even being in my last year of college, I am still struggling with the basics of statistics. With your help, I have been striving exponentially in class and looking to graduate from college in this semester. Your videos have been so so so helpful and i wish you an amazing health to continue with your content. I wish you could have been my professor in college. Thank you for putting out the high quality contents. Words can't describe how much I appreciate you, sir. Thank you. You have changed my life.
@ritvikmath3 жыл бұрын
Thanks for the kind words. Wishing you much success!
@sidddddddddddddd Жыл бұрын
What you've called the closed form of the covariance matrix is actually the biased estimator of the covariance matrix \Sigma. And if you divide by (N-1) instead of (N), you get the unbiased estimator of \Sigma. Awesone video! Thanks :D
@MaxDavidsonArgentina4 жыл бұрын
Thanks for sharing your knowledge. It's great to have people like you helping out!
@zilezile49424 жыл бұрын
Good morning If you have difficulty understanding the statistical models and programming them with the R software; You have difficulty understanding where the main components come from when you do principal component analysis; You need to discover the statistics for functional data in particular the analysis in functional principal components; you have no idea how to model by functional linear model ... You like clear and detailed explanations. Click on this link amikour.wordpress.com/nos-formations/
@alphar853 жыл бұрын
I stopped at 01:33 and I am going to watch the other 5 videos. you are such a blessing mate.
@cameronbaird5658 Жыл бұрын
Phenomenal video, thank you for the hard work 👏
@subhabhadra619 Жыл бұрын
Awesomely represented..
@nuamaaniqbal63732 жыл бұрын
cant thank u enough!! u r truly the boss!
@yarenlerler67 Жыл бұрын
Ahh such a clean explanation. I really appreciate! I will have practical statics for astrophysics exam soon, and I was having some problem with the theory part. All your videos were very helpful! I hope I am gonna get a good grade from the exam. :)
@christinejiang63863 ай бұрын
wow! thank you! I watched all the videos before watching this one, they really helps a lot!
@Tankwell-cq5ky2 жыл бұрын
Very well presented - well done!😊😊
@133839297 Жыл бұрын
You have a gift for teaching.
@arun_kanthali2 жыл бұрын
Great Explanation.. Thank-you 👍
@mashakozlovtseva43784 жыл бұрын
Everything was clearly understood from math side! Thank you for your link on Medium account!
@zilezile49424 жыл бұрын
Good morning If you have difficulty understanding the statistical models and programming them with the R software; You have difficulty understanding where the main components come from when you do principal component analysis; You need to discover the statistics for functional data in particular the analysis in functional principal components; you have no idea how to model by functional linear model ... You like clear and detailed explanations. Click on this link amikour.wordpress.com/nos-formations/
@ShubhamYadav-ut9hoАй бұрын
Amazing explanation as always
@cll259811 күн бұрын
Epic explanation
@muhammadghazy9941 Жыл бұрын
thank you man appreciate it
@resoluation3453 ай бұрын
The best series to explain the maths behind PCA
@aravindsaraswatula256112 күн бұрын
Awesome video
@Sriram-kj6kl2 жыл бұрын
Your videos help a lot man.. Thank you 👍
@rajathjain3144 жыл бұрын
Very Intuitive, Great Job Ritvik!
@zilezile49424 жыл бұрын
Good morning If you have difficulty understanding the statistical models and programming them with the R software; You have difficulty understanding where the main components come from when you do principal component analysis; You need to discover the statistics for functional data in particular the analysis in functional principal components; you have no idea how to model by functional linear model ... You like clear and detailed explanations. Click on this link amikour.wordpress.com/nos-formations/
@Chill_Magma9 ай бұрын
Straight to the point and thorough you deserve to be subscribed from my 3 accounts
@mmarva35972 жыл бұрын
Thank you very much !! really helpful
@_arkadij4 ай бұрын
Very appreciative of the explanation why we end up with using vectors corresponding to the biggest Eigenvalues. Thanks so much
@berkoec3 жыл бұрын
Such a well-explained video - keep up the great work!
@ritvikmath3 жыл бұрын
Thanks a ton!
@vinceb80413 жыл бұрын
12:20 Quick note on why going down the list of eigenvalues is legit, the covariance matrix is a symmetric matrix, and it can be shown that if such a matrix has more than one eigenvalues that are not the same, the corresponding eigenvectors will be orthogonal.
@deplo3 жыл бұрын
Hi Ritvikmath, thank you for your super informative videos! I took all courses on this topic but I was wondering if you could expand it with factor analysis and correspondence analysis. It would be interesting to know how different methods work and relate to each other because it would provide a deeper perspective. Thanks
@DeRocks160721 күн бұрын
You are great teacher.. ultimately I understood
@herberthubert68283 жыл бұрын
you rock, thank you
@proxyme3628 Жыл бұрын
Brilliant explanation of why eigen vector is the one from maximum optimisation, never saw such great explanation before. Wish your course is in Coursera. I do not think any text book explains the eigen value as Lagrangian Multiplier and eigen vector as maximising variance. Thanks so much.
@pratik.patil876 ай бұрын
Thanks Ritvik, I went through multiple resources to figure out this exact questions " why does eigen vectors and eigen values of a covariance matrix represent the direction and strength of the biggest increase in variance" . Thanks your video clarifies it beautifully. One question still though, I understand the equation we use to maximise but why do we need the constraint(uT u =1)?
@ahmadawad47824 жыл бұрын
Watched many videos about linear algebra and PCA. You're the one who made it clear for me. Thanks!
@zilezile49424 жыл бұрын
Good morning If you have difficulty understanding the statistical models and programming them with the R software; You have difficulty understanding where the main components come from when you do principal component analysis; You need to discover the statistics for functional data in particular the analysis in functional principal components; you have no idea how to model by functional linear model ... You like clear and detailed explanations. Click on this link amikour.wordpress.com/nos-formations/
@Chill_Magma9 ай бұрын
Seeing your videos increases my confidence on math stuff :DDD
@santiagolicea3814 Жыл бұрын
This is a great explanation, thanks a lot. It'll be great if you can also make a video showing a practical example with some data set, showing how you use the eigenvectors projection matrix to transform the initial data set.
@erfanbayat3974Ай бұрын
this video is amazing
@volsurf12743 жыл бұрын
Concise, clear and superbly explained. Thanks!
@ritvikmath3 жыл бұрын
Glad it was helpful!
@gc63274 жыл бұрын
Hi Ritvik- Can you do a video on factor analysis. That would be huge! Thanks buddy!
@fahimfaisal46602 жыл бұрын
Excellent
@ernestanonde32182 жыл бұрын
great video
@Cybrean13 жыл бұрын
Excellent presentation and delivery … wish you all the success!
@ritvikmath3 жыл бұрын
Thank you! You too!
@543phi4 жыл бұрын
Thanks for this video! As a Data Science student, your lecture helped to clarify a lot....I appreciate your teaching style.
@zilezile49424 жыл бұрын
Good morning If you have difficulty understanding the statistical models and programming them with the R software; You have difficulty understanding where the main components come from when you do principal component analysis; You need to discover the statistics for functional data in particular the analysis in functional principal components; you have no idea how to model by functional linear model ... You like clear and detailed explanations. Click on this link amikour.wordpress.com/nos-formations/
@georgegkenios4863 жыл бұрын
Amazing work mate!
@ritvikmath3 жыл бұрын
Thanks a lot!
@ajanasoufiane39034 жыл бұрын
Great video, it would be nice if you could show the big picture through the SVD decomposition :)
@zilezile49424 жыл бұрын
Good morning If you have difficulty understanding the statistical models and programming them with the R software; You have difficulty understanding where the main components come from when you do principal component analysis; You need to discover the statistics for functional data in particular the analysis in functional principal components; you have no idea how to model by functional linear model ... You like clear and detailed explanations. Click on this link amikour.wordpress.com/nos-formations/
@rabiizahir28852 жыл бұрын
Thanks a lot.
@nandhinin7994 жыл бұрын
Clearly explained, helped me greatly in understanding the basis of PCA.
@zilezile49424 жыл бұрын
Good morning If you have difficulty understanding the statistical models and programming them with the R software; You have difficulty understanding where the main components come from when you do principal component analysis; You need to discover the statistics for functional data in particular the analysis in functional principal components; you have no idea how to model by functional linear model ... You like clear and detailed explanations. Click on this link amikour.wordpress.com/nos-formations/
@Rockyzach88 Жыл бұрын
Just finished the LA section in the Deep Learning book and I can tell this is going to help supplement and fill in this gaps of understanding. Good vid.
@ritvikmath Жыл бұрын
I hope so!
@alejandropalaciosgarcia27673 жыл бұрын
Bro, you are awsome
@simranjoharle4220 Жыл бұрын
Your videos are extremely helpful! Thank you!
@ritvikmath Жыл бұрын
Glad you like them!
@jhonportella56183 жыл бұрын
Great, great video I really appreciate your effort and good methodology to teach. I have a question on the projection math. on your projection video you obtained P=XUU but here you used P=U*XU. Maybe this is a silly question but I would really appreciate if you can tell me why this equivalence is possible. Many thanks
@brianogrady373 күн бұрын
I wish you specified what values represented the Principal Conponents earlier on. But great video regardless.
@user-xw5cg7by6t Жыл бұрын
This video is super great! I was wondering why Covariance matrix is used to compute PCA, but this video made my doubts clear!!
@ritvikmath Жыл бұрын
Glad it was helpful!
@user-kw6ib6ks1q3 ай бұрын
great explanation. Really appreciate it. thanks
@ritvikmath3 ай бұрын
Glad it was helpful!
@fabianwinkelmann39313 жыл бұрын
Thank you:)
@akrylic_4 жыл бұрын
There's a property of transposes around 6:45 that you could have mentioned, and I got tripped up for a second. The reason why you can write u^T*(xi-xbar) as (xi-xbar) ^T*u is because (AB)^T =(B^T)(A^T) It's a cool trick, but not obvious
@ritvikmath4 жыл бұрын
Very true, thanks for filling in the missing step!
@zechengchang34443 жыл бұрын
Can you explain more? How does (AB)^T =(B^T)(A^T) have anything to do with u^T*(xi-xbar)? Thanks.
@odysseashlap4 жыл бұрын
Really appreciate this! Any good book suggestion for PCA mathematical Framework in greater depth? Maybe another video (hard maths of pca)?
@AshishKGor2 жыл бұрын
Thanks sir.
@darshansolanki55354 жыл бұрын
Best video!!
@zilezile49424 жыл бұрын
Good morning If you have difficulty understanding the statistical models and programming them with the R software; You have difficulty understanding where the main components come from when you do principal component analysis; You need to discover the statistics for functional data in particular the analysis in functional principal components; you have no idea how to model by functional linear model ... You like clear and detailed explanations. Click on this link amikour.wordpress.com/nos-formations/
@seetaramdantu31903 жыл бұрын
excellent...well explained
@ritvikmath3 жыл бұрын
Glad it was helpful!
@chinaminer3 жыл бұрын
Hello, thanks for this video and also for the others, well done! On this video I have a doubt to ask. Where can I submit the question in order to not mess comments here?
@PR-ud4fp Жыл бұрын
Thanks 😊
@suvikarhu46272 жыл бұрын
@ritvikmath 5:02 I don't understand where is this formula of projection (proj(xi)=ut xi u) coming from. The projection video does not say that. What the projection video exactly says is that the proj(xi) = (xi dot u)*u. No transpose there! Where did you get that transpose from? And the dot product is missing ? Another question, at 5:50 why do you take only the magnitude of the vector?
@ahmad38232 ай бұрын
Amazing
@ritvikmath2 ай бұрын
Thank you! Cheers!
@knp43564 жыл бұрын
Hey Ritvik, It would be great if you can generate some problems for viewers to solve. Watching is great but if you can supplement with actual problems then it would drive the points into viewers head. You can then further post solutions on your medium site. Hopefully at least 4-5 problems per each video. I've watched many videos on DS subjects but something in your teaching method is making it simpler to understand. Thanks.
@ritvikmath4 жыл бұрын
I honestly really appreciate that you're trying to help me be more effective at what I do. I think it's a great idea and I'll look into it. Thanks :)
@GeoffryGifari2 күн бұрын
Hmmm i noticed that if two categories are strongly correlated, the plot will look close to a straight line. Going to multidimensional space, that "line" looks like the vector u1 in the video, on which the data are projected. Does that mean PCA will perform better the more correlated two (or more) categories are?
@MohamedMostafa-kg6gk3 жыл бұрын
Thank you for this great explanation .
@ritvikmath3 жыл бұрын
You are welcome!
@brofessorsbooks33524 жыл бұрын
Good!
@zilezile49424 жыл бұрын
Good morning If you have difficulty understanding the statistical models and programming them with the R software; You have difficulty understanding where the main components come from when you do principal component analysis; You need to discover the statistics for functional data in particular the analysis in functional principal components; you have no idea how to model by functional linear model ... You like clear and detailed explanations. Click on this link amikour.wordpress.com/nos-formations/
@dr.kingschultz2 жыл бұрын
Do you have a video about instrumental variables? Because in general seems to be just regular manipulation, but in a more complex way. Also, do you have videos applying this concepts? Could be using R or Python. That would be very nice.
@mainakmukherjee344410 ай бұрын
We find the equation of the variance of the vector, on which we are going to project the data, and then tried maximizing it, because, the vector, for which the variance will be highest (max eigen value), is gonna retain most of the information of the data, after dimensionality reduction.
@quark37 Жыл бұрын
Fun video. Thank-you. And thanks for all the pre-req videos. Question: I've seen other videos that describe PCA vectors as orthogonal, but using eigenvectors they would not necessarily be orthogonal, right? What is the correct way to think about the orthogonality of PCA vectors? Thanks. * I think I answered my own question. The eigenvectors in question are of the covariance matrix of the related variables. This matrix is symmetrical so the eigenvectors will be orthogonal. Correct?
@thirumurthym79803 жыл бұрын
@ 4.54 - you are referring about projection video - on how you arrive projections formula. There is no such mention of U transpose in that projections video.
@yurongluo4473 ай бұрын
Your video is helpful for us. Can you create one video to explain Independent Component Analysis in detail? Thanks.
@nirjasmuhammed3 жыл бұрын
thank u sir
@RealLifeKyurem3 жыл бұрын
Since principal component analysis is used to reduce the dimensions, thus lessen the curse of dimensionality, can you calculate the maximum amount of dimensions you need for a given dataset to find patterns?
@XXZSaikou2 ай бұрын
nicely explained! but I noticed you didn't mention the need to standardize the original data for PCA. Is standardization a little trick to make things faster or is it needed in the underlying math?
@diegolazareno80204 жыл бұрын
Never stop making these videos!!! One of Logistic Regression would be nice
@ritvikmath4 жыл бұрын
Hey I appreciate the kind words! I do have a vid on logistic regression here: kzbin.info/www/bejne/b6vaaGmGiZinsNU
@kisholoymukherjee Жыл бұрын
Hi ritvik, thanks for the video. Can you please tell me how the vector projection formula is being used to calculate the projection of xi on u here? The formulae in the two videos seem to be quite different. Would really appreciate if you could help understand the underlying math
@ArpitAnand-yd7tr Жыл бұрын
That's just a dot product between the potential u1 and Xi. It gives the magnitude of the projection in the direction of the unit vector u
@haseebali5123 жыл бұрын
Hi Ritvik- Great video! In the first part of the video, you impose the constraint that u is a unit vector. So to arrive at the maximization problem, we have already imposed this constraint. Does this introduce any logical problems?
@DarkShadow-tm2dk3 жыл бұрын
Unit vector is just because we want the direction that maximises the variance there is no problem infact is simplifies the maths
@AG-dt7we3 ай бұрын
Thanks for such amazing videos. Have 1 question: In the projection video you derived projection as X. V / ||V|| * u here you took started with u1T Xi u. What is the difference? Will be helpful if you can point me to some resources !