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### Reversing PCA back to the original variables [duplicate]

I have a set of data that has $n$ samples described by $m$ variables. I do a PCA to reduce it to just 2 dimensions so I can make a nice 2D plot of the data. I understand that the $x,y$ coordinates (i....
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### How to remove technical noise by discarding several leading PCA components? [duplicate]

I have a large metabolomics dataset, 6000 samples and 3300 features. For the samples the only thing that differentiates each sample from the rest is that one gene was knocked out, which will not ...
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### How do I remove the first principal component from a data set, while keeping it in the original coordinates? [duplicate]

I would like to remove the first principal component from a data set, but keep that data set in its original coordinates. I have taken a stab at this by taking PCA, zeroing the first PC, and then ...
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### Reconstruction of original dataset through loadings in PCA [duplicate]

I am very new to PCA and I was trying, just as excercize, to reconstruct original dataset from loadings. Let's suppose I have a matrix A corresponding to the original dataset and C that is the z-...
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### Reconstruct the data using first principal component [duplicate]

I would like to know how do I reconstruct the data using only the first principal component of the PCA?
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### How to recreate a particular image from PCA from a database of images? [duplicate]

Please forgive if this is a repeat but I couldn't find a similar question (at least as it pertains to me). I have a database of 30,000 images of digits (0-9). Every image is 28*28. So, every image is ...
657k views

### Making sense of principal component analysis, eigenvectors & eigenvalues

In today's pattern recognition class my professor talked about PCA, eigenvectors and eigenvalues. I understood the mathematics of it. If I'm asked to find eigenvalues etc. I'll do it correctly like ...
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### What are the differences between Factor Analysis and Principal Component Analysis?

It seems that a number of the statistical packages that I use wrap these two concepts together. However, I'm wondering if there are different assumptions or data 'formalities' that must be true to use ...
270k views

### 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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### PCA on correlation or covariance?

What are the main differences between performing principal component analysis (PCA) on the correlation matrix and on the covariance matrix? Do they give the same results?
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### How to project a new vector onto PCA space?

After performing principal component analysis (PCA), I want to project a new vector onto PCA space (i.e. find its coordinates in the PCA coordinate system). I have calculated PCA in R language using <...