23 questions linked to/from PCA and proportion of variance explained
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### Calculating PCA variance explained [duplicate]

I've read through this explanation here regarding calculating the variance explained from PCA output. I think I got it right but might be off in my interpretation of R output. In the example below, I ...
43k views

### What is percentage of variance in PCA? [duplicate]

I am trying to understand Principal Component Analysis (PCA). I found a webpage on PCA that introduces it and the concept of the percentage of variance. However, I am very confused about what "...
2k views

### What is proportion of variance explained in PCA? [duplicate]

Can anyone explain about the proportion of variance explained in PCA and why it is important in the analysis of PCA?
541k 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 ...
216k views

### 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 ...
204k 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 ...
7k views

### What can cause PCA to worsen results of a classifier?

I have a classifier that I'm doing cross-validation on, along with a hundred or so features that I'm doing forward selection on to find optimal combinations of features. I also compare this against ...
15k views

### Why PCA of data by means of SVD of the data?

This question is about an efficient way to compute principal components. Many texts on linear PCA advocate using singular-value decomposition of the casewise data. That is, if we have data $\bf X$ ...
18k views

### Why are principal components in PCA (eigenvectors of the covariance matrix) mutually orthogonal? [duplicate]

Why are principal components in PCA mutually orthogonal? I know that PCA can be calculated by eig(cov(X)), where X is centered. ...
9k views

### Questions on PCA: when are PCs independent? why is PCA sensitive to scaling? why are PCs constrained to be orthogonal?

I am trying to understand some descriptions of PCA (the first two are from Wikipedia), emphasis added: Principal components are guaranteed to be independent only if the data set is jointly normally ...
9k views

### Understanding cluster plot and component variability

I have run k-means clustering. I have also plotted the results using the following code in R: ...
6k views

### Proportion of explained variance in PCA and LDA

I have some basic questions regarding PCA (principal component analysis) and LDA (linear discriminant analysis): In PCA there is a way to calculate the proportion of variance explained. Is it also ...
1k views

### Why does log-transformation of the RNA-seq data reduce the amount of explained variance in PCA?

I am running a PCA on a dataset with 2k rows and 36k columns. I noticed that when I log-transform the data I need to ask for more principal components during PCA to achieve the same amount of ...
1k views

### Perform PCA. Extract PCs. Can one then tell what the most important _original_ features were, from the PCs? [duplicate]

Suppose that you have 1000 features, and a data set made up of say, 50,000 points. Suppose then that we perform PCA, and we extract the top 5 PCs, since they explain 99.99 percent of the variance, and ...
1k views

### Does the first principal component differ from simply computing the mean of all variables?

I was just wondering if the first principal component, while I am trying to find it for a dataset of 18 variables, is different from simply adding all variables and finding the mean? I.e. to compute ...

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