How do I efficiently find the mean and covariance of a cell containing matrices with different rows?

I have a 1x15 cell which contains 15 matrices, and each of them has different number of rows but all have the same number of columns(14). I am trying to find the mean and covariance of the whole cell. I compute them by concatenating all matrices into a big matrix and use the built-in functions to get the result, which looks like this:

 M = C{1};
 for i=2:15
    M = [M; C{i}];
 end
 mean = mean(M);
 cov = cov(M);

I think this approach does not fully take advantage of the flexibility of cells. Is there a more efficient way to do that?

Answer 1

[ANKUR KUMAR]

Use concatenate to mix up all cells into one matrix.

cat(2,C{:}); %you can use this only if number of rows must be same in all cells

or

cat(1,C{:});  %for your data

After using cat, calculate mean and cov in usual manner.

As your data have the different number of rows in all cells, you cannot use cat(2,C{:}).