11 questions linked to/from What is the intuition behind SVD?
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### Relationship between SVD and PCA. How to use SVD to perform PCA?

Principal component analysis (PCA) is usually explained via an eigen-decomposition of the covariance matrix. However, it can also be performed via singular value decomposition (SVD) of the data matrix ...
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### Statistics interview questions

I am looking for some statistics (and probability, I guess) interview questions, from the most basic through the more advanced. Answers are not necessary (although links to specific questions on this ...
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### What is the point of singular value decomposition?

I don't understand why reduction in dimension is important. What is the benefit of taking some data and reducing their dimension?
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### How does SVD save space?

We start with an $m \times n$ matrix before SVD. After SVD, we have three matrices of sizes, $m \times m$, $n \times n$ and $m \times n$. How do we save space then if now we have three matrices ...
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### What is the PCA representation of an image?

I've read a lot about how PCA is used to reduce the dimensionality of data, including this great answer and this mathy post. But I'm unclear what this does when applied to image data? For example, ...
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### Confirming an understanding of SVD

I'm trying to sift through the concepts of low-rank approximations, matrix factorizations and SVD. There's a lot of info out there and the rabbit hole is deep so I just want to make sure my high level ...
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### Principal Component Analysis: how to interpret the total contribution of variables on several dimensions

When we calculate the total contribution of a variable for a single dimension, the sum of all single contributions is equal to 100%, which makes perfect sense. The http://www.sthda.com suggests to ...
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### Importance of complicated linear algebra for data scientist [closed]

I've already finished Andrew Ng's machine learning course and now working with textbook 'The Elements of Statistical Learning'. I'm successfully implementing equations and concepts described there ...
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### When can I use $XX^\top/n$ as covariance matrix for PCA?

Given a data matrix $\mathbf X$, can I always obtain its covariance matrix (to use in PCA) by centering (subtracting the column means) and then computing $\mathbf X \mathbf X^\top/n$? Is this always ...