How to apply pca() [Matlab] on high dimensional data

I want to apply `pca()` function in `matlab` on data with `500 dimensions`. But pca() has a limit of only 99 dimensions. Do I have to write code for pca.

Answer 1

[the cyclist]

Why do you believe pca has such a limit?

p = pca(rand(1000,700));

runs just fine.

[Atinesh Singh]

Check for this

p = pca(rand(100,700));

[the cyclist]

May I suggest that you carefully read the documentation and this answer of mine, to get a better understanding of the syntax and output of pca?

[the cyclist]

I think I finally appreciate what you are missing.

You have more dimensions (p=700) than you have observations (n=100). When p>n, you can fully explain all the variation in the observations with n-1 principal components, which in your case is 99.

[Akash Reddy]

Thanks sir.

Answer 2

[John D'Errico]

I think the problem is you don't understand the PCA code, at least how to use the tool as provided. READ THE HELP! A problem with size 100x700 for the PCA function is a problem with 700 dimensions, not 100. PCA treats each ROW of the array as one sample, one observation.

Your question (coupled with your later comment) strongly implies that your array is simply transposed from what you need to pass into the PCA tool. Read the help for PCA.

[Atinesh Singh]

I've read the documentation. It's clearly mentioned that pca() takes input a matrix of dimension n-by-p where n = # observations and p = # variables and return a matrix with dimension p-by-p where each column is a principal component with decreasing variance. Hence when we pca() on matrix with dimension 100-by-700 it just return matrix with dimesnion 700-by-700.

[Walter Roberson]

It is not clear to me why you think that pca has a limit of 99 dimensions?

I had no problem at all a moment ago running pca on a 1000 x 1000 matrix.

Answer 3

[Taimour Hamayoun]

hello! i am new in matlab and i am try to apply PCA on my dataset of 19 dimensions and try to reduce it in 4 dimension but i didnt find the proper way plz guide and provide me a proper source with explanation thanx

[the cyclist]

May I suggest that you carefully read the documentation and this answer of mine, to get a better understanding of the syntax and output of pca?

Also, you posted this as an answer to a question. It would have garnered more attention as a new question, but I happened to see it.