# Tag Info

Accepted

### How to reverse PCA and reconstruct original variables from several principal components?

PCA computes eigenvectors of the covariance matrix ("principal axes") and sorts them by their eigenvalues (amount of explained variance). The centered data can then be projected onto these principal ...
• 106k

### Are there cases where PCA is more suitable than t-SNE?

$t$-SNE is a great piece of Machine Learning but one can find many reasons to use PCA instead of it. Of the top of my head, I will mention five. As most other computational methodologies in use, $t$-...
• 45.2k

### What are the differences between Factor Analysis and Principal Component Analysis?

A basic, yet a kind of painstaking, explanation of PCA vs Factor analysis with the help of scatterplots, in logical steps. (I thank @amoeba who, in his comment to the question, has encouraged me to ...
• 58.4k

### Building an autoencoder in Tensorflow to surpass PCA

Here is the key figure from the 2006 Science paper by Hinton and Salakhutdinov: It shows dimensionality reduction of the MNIST dataset ($28\times 28$ black and white images of single digits) from the ...
• 106k
Accepted

• 82.8k

### Can PC1 explain more than 90% of variance?

Certainly that can happen. Below is a simulated example in R with just two original variables. As long as they are strongly enough correlated, they can very well be summarized by the projection to a ...
• 129k
Accepted

### How does PCA behave when there is no correlation in the dataset?

If you have no observed correlation, then your covariance matrix is diagonal, and the PCA diagonalizes a matrix that is already diagonal (so it does nothing). If you have no population correlation but ...
• 65.7k