753k 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 ...
29k views

### Best PCA algorithm for huge number of features (>10K)?

I previously asked this on StackOverflow, but it seems like it might be more appropriate here, given that it didn't get any answers on SO. It's kind of at the intersection between statistics and ...
5k views

### Is large scale PCA even possible?

Principal component analysis' (PCA) classical way is to do it on an input data matrix which columns have zero mean (then PCA can "maximize variance"). This can be achieved easily by centering the ...
5k views

### PCA demeaning the data [duplicate]

What is the motivation for demeaning the data when doing PCA. I've been told to do it, but I've never heard a good and/or intuitive reason for it. Is this a case where doing it just makes the math ...
2k views

### Using the 'U' Matrix of SVD as Feature Reduction [duplicate]

This is a follow-up to the question asked regarding SVD and dimensionality reduction (question). In that question I asked how to use SVD for dimensionality reduction. Although not stated, the ...
2k views

### In PCA, do we have to center and normalize eigenvectors or solely to normalize them?

Given such a matrix about the grades of 6 students in Maths, Computer Sciences and French: A=\begin{bmatrix} 1 & 0 & 0 \\ 0 & 0 & 1 \\ 0 & 1 & 2 \\ 2 & 2 ...
722 views

### Scores are still correlated after PCA

I have 22 variables with more than 6000 observations. They are highly correlated. I know these data would work as great explanatory variables to a dichotomous event (present absent). Therefore, I ...
410 views

### Approaches to reduce dimensions (feature selection/extraction) with high dimensional count data before running tree based model

My dataset has ~100k samples and 3000 dimensions. The data are counts, anywhere between 0-8 and it's pretty sparse. Because of 'curse of high dimension', I want to shrink the number of variables ...
148 views

### Testing of hypothesis for the linearity of a data? PCA suggested, but how do we design a statistical test using it? [closed]

Suppose we're given the data set $\{x_1 \dots x_n\}$ in $\mathbb{R}^D$ the $D$-dimensional Euclidean space, and assume this data has intrinsic dimension $d < D.$ N.B. this just means that data is ...